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England H, Herdean A, Matthews J, Hughes DJ, Roper CD, Suggett DJ, Voolstra CR, Camp EF. A portable multi-taxa phenotyping device to retrieve physiological performance traits. Sci Rep 2024; 14:21826. [PMID: 39294209 PMCID: PMC11411053 DOI: 10.1038/s41598-024-71972-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 09/02/2024] [Indexed: 09/20/2024] Open
Abstract
Organismal phenotyping to identify fitness traits is transforming our understanding of adaptive responses and ecological interactions of species within changing environments. Here we present a portable Multi-Taxa Phenotyping (MTP) system that can retrieve a suite of metabolic and photophysiological parameter across light, temperature, and/or chemical gradients, using real time bio-optical (oxygen and chlorophyll a fluorescence) measurements. The MTP system integrates three well-established technologies for the first time: an imaging Pulse Amplitude Modulated (PAM) chlorophyll a fluorometer, custom-designed well plates equipped with optical oxygen sensors, and a thermocycler. We demonstrate the ability of the MTP system to distinguish phenotypic performance characteristics of diverse aquatic taxa spanning corals, mangroves and algae based on metabolic parameters and Photosystem II dynamics, in a high-throughput capacity and accounting for interactions of different environmental gradients on performance. Extracted metrics from the MTP system can not only provide information on the performance of aquatic taxa exposed to differing environmental gradients, but also provide predicted phenotypic responses of key aquatic organisms to environmental change. Further work validating how rapid phenotyping tools such as the MTP system predict phenotypic responses to long term environmental changes in situ are urgently required to best inform how these tools can support management efforts.
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Affiliation(s)
- Hadley England
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia.
| | - Andrei Herdean
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia
| | - Jennifer Matthews
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia
| | - David J Hughes
- National Sea Simulator, Australian Institute of Marine Science, Townsville, QLD, Australia
| | - Christine D Roper
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia
| | - David J Suggett
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia
- KAUST Reefscape Restoration Initiative (KRRI) and Red Sea Research Center (RSRC), King Abdullah University of Science and Technology, 23955, Thuwal, Saudi Arabia
| | | | - Emma F Camp
- University of Technology Sydney, Climate Change Cluster, Ultimo, NSW, 2007, Australia.
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2
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Yanarella CF, Fattel L, Lawrence-Dill CJ. Genome-wide association studies from spoken phenotypic descriptions: a proof of concept from maize field studies. G3 (BETHESDA, MD.) 2024; 14:jkae161. [PMID: 39099140 PMCID: PMC11373645 DOI: 10.1093/g3journal/jkae161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 06/23/2024] [Indexed: 08/06/2024]
Abstract
We present a novel approach to genome-wide association studies (GWAS) by leveraging unstructured, spoken phenotypic descriptions to identify genomic regions associated with maize traits. Utilizing the Wisconsin Diversity panel, we collected spoken descriptions of Zea mays ssp. mays traits, converting these qualitative observations into quantitative data amenable to GWAS analysis. First, we determined that visually striking phenotypes could be detected from unstructured spoken phenotypic descriptions. Next, we developed two methods to process the same descriptions to derive the trait plant height, a well-characterized phenotypic feature in maize: (1) a semantic similarity metric that assigns a score based on the resemblance of each observation to the concept of 'tallness' and (2) a manual scoring system that categorizes and assigns values to phrases related to plant height. Our analysis successfully corroborated known genomic associations and uncovered novel candidate genes potentially linked to plant height. Some of these genes are associated with gene ontology terms that suggest a plausible involvement in determining plant stature. This proof-of-concept demonstrates the viability of spoken phenotypic descriptions in GWAS and introduces a scalable framework for incorporating unstructured language data into genetic association studies. This methodology has the potential not only to enrich the phenotypic data used in GWAS and to enhance the discovery of genetic elements linked to complex traits but also to expand the repertoire of phenotype data collection methods available for use in the field environment.
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Affiliation(s)
- Colleen F Yanarella
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
| | - Leila Fattel
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics and Genomics Program, Iowa State University, Ames, IA 50011, USA
| | - Carolyn J Lawrence-Dill
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, USA
- Interdepartmental Genetics and Genomics Program, Iowa State University, Ames, IA 50011, USA
- Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
- College of Agriculture and Life Sciences, Iowa State University, Ames, IA 50011, USA
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3
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Lee AMJ, Foong MYM, Song BK, Chew FT. Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2024; 44:60. [PMID: 39267903 PMCID: PMC11391014 DOI: 10.1007/s11032-024-01497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 09/01/2024] [Indexed: 09/15/2024]
Abstract
To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-024-01497-2.
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Affiliation(s)
- Adrian Ming Jern Lee
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
| | - Melissa Yuin Mern Foong
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Beng Kah Song
- School of Science, Monash University Malaysia, Bandar Sunway, 47500 Subang Jaya, Selangor Darul Ehsan Malaysia
| | - Fook Tim Chew
- Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore, 117543 Republic of Singapore
- NUS Agritech Centre, National University of Singapore, 85 Science Park Dr, #01-03, Singapore, 118258 Republic of Singapore
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4
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Zhang Z, Qu Y, Ma F, Lv Q, Zhu X, Guo G, Li M, Yang W, Que B, Zhang Y, He T, Qiu X, Deng H, Song J, Liu Q, Wang B, Ke Y, Bai S, Li J, Lv L, Li R, Wang K, Li H, Feng H, Huang J, Yang W, Zhou Y, Song CP. Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat. THE NEW PHYTOLOGIST 2024; 243:1758-1775. [PMID: 38992951 DOI: 10.1111/nph.19942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 04/19/2024] [Indexed: 07/13/2024]
Abstract
Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.
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Affiliation(s)
- Zhen Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Yunfeng Qu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Feifei Ma
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Qian Lv
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Xiaojing Zhu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Guanghui Guo
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Mengmeng Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Wei Yang
- School of Computer and Information Engineering, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Beibei Que
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Yun Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Tiantian He
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Xiaolong Qiu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hui Deng
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qian Liu
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Baoqi Wang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Youlong Ke
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Shenglong Bai
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Jingyao Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Linlin Lv
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Ranzhe Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Kai Wang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hao Li
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinling Huang
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
- Department of Biology, East Carolina University, Greenville, NC, 27858, USA
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yun Zhou
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
- Academy for Advanced Interdisciplinary Studies, Henan University, Kaifeng, 475004, China
| | - Chun-Peng Song
- State Key Laboratory of Crop Stress Adaptation and Improvement, College of Agriculture, School of Life Sciences, Henan University, Jinming Ave 1, Kaifeng, 475004, China
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5
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Vazquez DV, Spetale FE, Nankar AN, Grozeva S, Rodríguez GR. Machine Learning-Based Tomato Fruit Shape Classification System. PLANTS (BASEL, SWITZERLAND) 2024; 13:2357. [PMID: 39273841 PMCID: PMC11397308 DOI: 10.3390/plants13172357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/21/2024] [Accepted: 08/22/2024] [Indexed: 09/15/2024]
Abstract
Fruit shape significantly impacts the quality and commercial value of tomatoes (Solanum lycopersicum L.). Precise grading is essential to elucidate the genetic basis of fruit shape in breeding programs, cultivar descriptions, and variety registration. Despite this, fruit shape classification is still primarily based on subjective visual inspection, leading to time-consuming and labor-intensive processes prone to human error. This study presents a novel approach incorporating machine learning techniques to establish a robust fruit shape classification system. We trained and evaluated seven supervised machine learning algorithms by leveraging a public dataset derived from the Tomato Analyzer tool and considering the current four classification systems as label variables. Subsequently, based on class-specific metrics, we derived a novel classification framework comprising seven discernible shape classes. The results demonstrate the superiority of the Support Vector Machine model in terms of its accuracy, surpassing human classifiers across all classification systems. The new classification system achieved the highest accuracy, averaging 88%, and maintained a similar performance when validated with an independent dataset. Positioned as a common standard, this system contributes to standardizing tomato fruit shape classification, enhancing accuracy, and promoting consensus among researchers. Its implementation will serve as a valuable tool for overcoming bias in visual classification, thereby fostering a deeper understanding of consumer preferences and facilitating genetic studies on fruit shape morphometry.
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Affiliation(s)
- Dana V Vazquez
- Instituto de Investigaciones en Ciencias Agrarias de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Rosario (IICAR-CONICET-UNR), Campo Experimental Villarino, Zavalla S2125ZAA, Argentina
- Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Parque Villarino, CC Nº 14, Zavalla S2125ZAA, Argentina
| | - Flavio E Spetale
- Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Rosario (CIFASIS-CONICET-UNR), 27 de Febrero 210 bis, Rosario S2000EZP, Argentina
| | - Amol N Nankar
- Horticulture Department, University of Georgia, Tifton, GA 31793, USA
| | - Stanislava Grozeva
- Maritsa Vegetable Crops Research Institute (MVCRI), 4003 Plovdiv, Bulgaria
| | - Gustavo R Rodríguez
- Instituto de Investigaciones en Ciencias Agrarias de Rosario, Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de Rosario (IICAR-CONICET-UNR), Campo Experimental Villarino, Zavalla S2125ZAA, Argentina
- Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Parque Villarino, CC Nº 14, Zavalla S2125ZAA, Argentina
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6
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Sun S, Zhu Y, Liu S, Chen Y, Zhang Y, Li S. An integrated method for phenotypic analysis of wheat based on multi-view image sequences: from seedling to grain filling stages. FRONTIERS IN PLANT SCIENCE 2024; 15:1459968. [PMID: 39224846 PMCID: PMC11366606 DOI: 10.3389/fpls.2024.1459968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
Wheat exhibits complex characteristics during its growth, such as extensive tillering, slender and soft leaves, and severe organ cross-obscuration, posing a considerable challenge in full-cycle phenotypic monitoring. To address this, this study presents a synthesized method based on SFM-MVS (Structure-from-Motion, Multi-View Stereo) processing for handling and segmenting wheat point clouds, covering the entire growth cycle from seedling to grain filling stages. First, a multi-view image acquisition platform was constructed to capture image sequences of wheat plants, and dense point clouds were generated using SFM-MVS technology. High-quality dense point clouds were produced by implementing improved Euclidean clustering combined with centroids, color filtering, and statistical filtering methods. Subsequently, the segmentation of wheat plant stems and leaves was performed using the region growth segmentation algorithm. Although segmentation performance was suboptimal during the tillering, jointing, and booting stages due to the glut leaves and severe overlap, there was a salient improvement in wheat leaf segmentation efficiency over the entire growth cycle. Finally, phenotypic parameters were analyzed across different growth stages, comparing automated measurements of plant height, leaf length, and leaf width with actual measurements. The results demonstrated coefficients of determination (R 2 ) of 0.9979, 0.9977, and 0.995; root mean square errors (RMSE) of 1.0773 cm, 0.2612 cm, and 0.0335 cm; and relative root mean square errors (RRMSE) of 2.1858%, 1.7483%, and 2.8462%, respectively. These results validate the reliability and accuracy of our proposed workflow in processing wheat point clouds and automatically extracting plant height, leaf length, and leaf width, indicating that our 3D reconstructed wheat model achieves high precision and can quickly, accurately, and non-destructively extract phenotypic parameters. Additionally, plant height, convex hull volume, plant surface area, and Crown area were extracted, providing a detailed analysis of dynamic changes in wheat throughout its growth cycle. ANOVA was conducted across different cultivars, accurately revealing significant differences at various growth stages. This study proposes a convenient, rapid, and quantitative analysis method, offering crucial technical support for wheat plant phenotypic analysis and growth dynamics monitoring, applicable for precise full-cycle phenotypic monitoring of wheat.
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Affiliation(s)
- Shengxuan Sun
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yeping Zhu
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Shengping Liu
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yongkuai Chen
- Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou, Fujian, China
| | - Yihan Zhang
- College of Letters & Science, University of California, Davis, Davis, CA, United States
| | - Shijuan Li
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, China
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7
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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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8
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Sun J, Ren Z, Cui J, Tang C, Luo T, Yang W, Song P. A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0213. [PMID: 39091338 PMCID: PMC11292034 DOI: 10.34133/plantphenomics.0213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/19/2024] [Indexed: 08/04/2024]
Abstract
Rice panicle traits serve as critical indicators of both yield potential and germplasm resource quality. However, traditional manual measurements of these traits, which typically involve threshing, are not only laborious and time-consuming but also prone to introducing measurement errors. This study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length. To address the challenge of grain occlusion within an intact panicle, we define a panicle morphology index to quantify the occlusion levels among the rice grains within the panicle. By calibrating the grain number obtained directly from rice panicle images based on the panicle morphology index, we substantially improve the grain number detection accuracy. For measuring grain length and width, the EOPT selects rice grains using an intersection over union threshold of 0.8 and a confidence threshold of 0.7 during the grain detection process. The mean values of these grains were calculated to represent all the panicle grain lengths and widths. In addition, EOPT extracted the main path of the skeleton of the rice panicle using the Astar algorithm to determine panicle lengths. Validation on a dataset of 1,554 panicle images demonstrated the effectiveness of the proposed method, achieving 93.57% accuracy in panicle grain counting with a mean absolute percentage error of 6.62%. High accuracy rates were also recorded for grain length (96.83%) and panicle length (97.13%). Moreover, the utility of EOPT was confirmed across different years and scenes, both indoors and outdoors. A genome-wide association study was conducted, leveraging the phenotypic traits obtained via EOPT and genotypic data. This study identified single-nucleotide polymorphisms associated with grain length, width, number per panicle, and panicle length, further emphasizing the utility and potential of this method in advancing rice breeding.
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Affiliation(s)
| | | | | | | | | | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research,
Huazhong Agricultural University, Wuhan 430070, PR China
| | - Peng Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research,
Huazhong Agricultural University, Wuhan 430070, PR China
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9
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Jiang N, Zhu XG. Modern phenomics to empower holistic crop science, agronomy, and breeding research. J Genet Genomics 2024; 51:790-800. [PMID: 38734136 DOI: 10.1016/j.jgg.2024.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 04/25/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.
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Affiliation(s)
- Ni Jiang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Xin-Guang Zhu
- Center of Excellence for Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
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10
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Liu L, Zhan J, Yan J. Engineering the future cereal crops with big biological data: toward intelligence-driven breeding by design. J Genet Genomics 2024; 51:781-789. [PMID: 38531485 DOI: 10.1016/j.jgg.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/17/2024] [Accepted: 03/17/2024] [Indexed: 03/28/2024]
Abstract
How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.
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Affiliation(s)
- Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
| | - Jimin Zhan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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11
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Kim Y, Abebe AM, Kim J, Hong S, An K, Shim J, Baek J. Deep learning-based elaiosome detection in milk thistle seed for efficient high-throughput phenotyping. FRONTIERS IN PLANT SCIENCE 2024; 15:1395558. [PMID: 39129764 PMCID: PMC11310567 DOI: 10.3389/fpls.2024.1395558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 07/04/2024] [Indexed: 08/13/2024]
Abstract
Milk thistle, Silybum marianum (L.), is a well-known medicinal plant used for the treatment of liver diseases due to its high content of silymarin. The seeds contain elaiosome, a fleshy structure attached to the seeds, which is believed to be a rich source of many metabolites including silymarin. Segmentation of elaiosomes using only image analysis is difficult, and this makes it impossible to quantify the elaiosome phenotypes. This study proposes a new approach for semi-automated detection and segmentation of elaiosomes in milk thistle seed using the Detectron2 deep learning algorithm. One hundred manually labeled images were used to train the initial elaiosome detection model. This model was used to predict elaiosome from new datasets, and the precise predictions were manually selected and used as new labeled images for retraining the model. Such semi-automatic image labeling, i.e., using the prediction results of the previous stage for retraining the model, allowed the production of sufficient labeled data for retraining. Finally, a total of 6,000 labeled images were used to train Detectron2 for elaiosome detection and attained a promising result. The results demonstrate the effectiveness of Detectron2 in detecting milk thistle seed elaiosomes with an accuracy of 99.9%. The proposed method automatically detects and segments elaiosome from the milk thistle seed. The predicted mask images of elaiosome were used to analyze its area as one of the seed phenotypic traits along with other seed morphological traits by image-based high-throughput phenotyping in ImageJ. Enabling high-throughput phenotyping of elaiosome and other seed morphological traits will be useful for breeding milk thistle cultivars with desirable traits.
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Affiliation(s)
- Younguk Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Alebel Mekuriaw Abebe
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Jaeyoung Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | - Suyoung Hong
- Genomics Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
| | | | | | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea
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12
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Awada L, Phillips PWB, Bodan AM. The evolution of plant phenomics: global insights, trends, and collaborations (2000-2021). FRONTIERS IN PLANT SCIENCE 2024; 15:1410738. [PMID: 39104843 PMCID: PMC11298374 DOI: 10.3389/fpls.2024.1410738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/08/2024] [Indexed: 08/07/2024]
Abstract
Introduction Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications. Methods The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET. Results Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges. Discussion The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.
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Affiliation(s)
- Lana Awada
- Centre for the Study of Science and Innovation Policy, Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, Saskatoon, SK, Canada
| | - Peter W. B. Phillips
- Centre for the Study of Science and Innovation Policy, Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ana Maria Bodan
- Canadian Hub for Applied and Social Research, University of Saskatchewan, Saskatoon, SK, Canada
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13
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Maulenbay A, Rsaliyev A. Fungal Disease Tolerance with a Focus on Wheat: A Review. J Fungi (Basel) 2024; 10:482. [PMID: 39057367 PMCID: PMC11277790 DOI: 10.3390/jof10070482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/10/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
In this paper, an extensive review of the literature is provided examining the significance of tolerance to fungal diseases in wheat amidst the escalating global demand for wheat and threats from environmental shifts and pathogen movements. The current comprehensive reliance on agrochemicals for disease management poses risks to food safety and the environment, exacerbated by the emergence of fungicide resistance. While resistance traits in wheat can offer some protection, these traits do not guarantee the complete absence of losses during periods of vigorous or moderate disease development. Furthermore, the introduction of individual resistance genes into wheat monoculture exerts selection pressure on pathogen populations. These disadvantages can be addressed or at least mitigated with the cultivation of tolerant varieties of wheat. Research in this area has shown that certain wheat varieties, susceptible to severe infectious diseases, are still capable of achieving high yields. Through the analysis of the existing literature, this paper explores the manifestations and quantification of tolerance in wheat, discussing its implications for integrated disease management and breeding strategies. Additionally, this paper addresses the ecological and evolutionary aspects of tolerance in the pathogen-plant host system, emphasizing its potential to enhance wheat productivity and sustainability.
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Affiliation(s)
- Akerke Maulenbay
- Research Institute for Biological Safety Problems, Gvardeisky 080409, Kazakhstan
| | - Aralbek Rsaliyev
- Research Institute for Biological Safety Problems, Gvardeisky 080409, Kazakhstan
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14
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Heckman RW, Pereira CG, Aspinwall MJ, Juenger TE. Physiological Responses of C 4 Perennial Bioenergy Grasses to Climate Change: Causes, Consequences, and Constraints. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:737-769. [PMID: 38424068 DOI: 10.1146/annurev-arplant-070623-093952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
C4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C4 perennial bioenergy grasses are predicted to thrive under climate change-C4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO2], high temperature, and drought-although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C4 physiology with recent advances in crop improvement, especially genomic selection.
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Affiliation(s)
- Robert W Heckman
- Rocky Mountain Research Station, US Department of Agriculture Forest Service, Cedar City, Utah, USA;
| | - Caio Guilherme Pereira
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | - Thomas E Juenger
- Department of Integrative Biology, University of Texas at Austin, Austin, Texas, USA;
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15
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Numajiri Y, Yoshida S, Hayashi T, Uga Y. Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice. ANNALS OF BOTANY 2024:mcae101. [PMID: 38908006 DOI: 10.1093/aob/mcae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND AND AIMS Root system architecture (RSA) plays a key role in plant adaptation to drought because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, as early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional (3D) image analysis. METHODS We used 109 F12 recombinant inbred lines (RILs) obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions. KEY RESULTS We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4 cm under drought conditions and between 0.0-1.7 cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others. CONCLUSIONS Fine classification of soil layers using 3D image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.
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Affiliation(s)
- Yuko Numajiri
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kan-non-dai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Saki Yoshida
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kan-non-dai, Tsukuba, Ibaraki, 305-8518, Japan
| | - Takeshi Hayashi
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 2-14-1 Nishi-shinbashi, Minato-ku, Tokyo, 105-0003, Japan
| | - Yusaku Uga
- Institute of Crop Science, National Agriculture and Food Research Organization, 2-1-2, Kan-non-dai, Tsukuba, Ibaraki, 305-8518, Japan
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16
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Peters Haugrud AR, Achilli AL, Martínez-Peña R, Klymiuk V. Future of durum wheat research and breeding: Insights from early career researchers. THE PLANT GENOME 2024:e20453. [PMID: 38760906 DOI: 10.1002/tpg2.20453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 05/20/2024]
Abstract
Durum wheat (Triticum turgidum ssp. durum) is globally cultivated for pasta, couscous, and bulgur production. With the changing climate and growing world population, the need to significantly increase durum production to meet the anticipated demand is paramount. This review summarizes recent advancements in durum research, encompassing the exploitation of existing and novel genetic diversity, exploration of potential new diversity sources, breeding for climate-resilient varieties, enhancements in production and management practices, and the utilization of modern technologies in breeding and cultivar development. In comparison to bread wheat (T. aestivum), the durum wheat community and production area are considerably smaller, often comprising many small-family farmers, notably in African and Asian countries. Public breeding programs such as the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) play a pivotal role in providing new and adapted cultivars for these small-scale growers. We spotlight the contributions of these and others in this review. Additionally, we offer our recommendations on key areas for the durum research community to explore in addressing the challenges posed by climate change while striving to enhance durum production and sustainability. As part of the Wheat Initiative, the Expert Working Group on Durum Wheat Genomics and Breeding recognizes the significance of collaborative efforts in advancing toward a shared objective. We hope the insights presented in this review stimulate future research and deliberations on the trajectory for durum wheat genomics and breeding.
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Affiliation(s)
- Amanda R Peters Haugrud
- Cereal Crops Research Unit, Edward T. Schafer Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, Fargo, North Dakota, USA
| | - Ana Laura Achilli
- Faculty of Land and Food Systems, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Raquel Martínez-Peña
- Regional Institute of Agri-Food and Forestry Research and Development of Castilla-La Mancha (IRIAF), Agroenvironmental Research Center El Chaparrillo, Ciudad Real, Spain
| | - Valentyna Klymiuk
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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17
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Wang B, Yang C, Zhang J, You Y, Wang H, Yang W. IHUP: An Integrated High-Throughput Universal Phenotyping Software Platform to Accelerate Unmanned-Aerial-Vehicle-Based Field Plant Phenotypic Data Extraction and Analysis. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0164. [PMID: 39165669 PMCID: PMC11335093 DOI: 10.34133/plantphenomics.0164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 03/07/2024] [Indexed: 08/22/2024]
Abstract
With the threshold for crop growth data collection having been markedly decreased by sensor miniaturization and cost reduction, unmanned aerial vehicle (UAV)-based low-altitude remote sensing has shown remarkable advantages in field phenotyping experiments. However, the requirement of interdisciplinary knowledge and the complexity of the workflow have seriously hindered researchers from extracting plot-level phenotypic data from multisource and multitemporal UAV images. To address these challenges, we developed the Integrated High-Throughput Universal Phenotyping (IHUP) software as a data producer and study accelerator that included 4 functional modules: preprocessing, data extraction, data management, and data analysis. Data extraction and analysis requiring complex and multidisciplinary knowledge were simplified through integrated and automated processing. Within a graphical user interface, users can compute image feature information, structural traits, and vegetation indices (VIs), which are indicators of morphological and biochemical traits, in an integrated and high-throughput manner. To fulfill data requirements for different crops, extraction methods such as VI calculation formulae can be customized. To demonstrate and test the composition and performance of the software, we conducted case-related rice drought phenotype monitoring experiments. In combination with a rice leaf rolling score predictive model, leaf rolling score, plant height, VIs, fresh weight, and drought weight were efficiently extracted from multiphase continuous monitoring data. Despite the significant impact of image processing during plot clipping on processing efficiency, the software can extract traits from approximately 500 plots/min in most application cases. The software offers a user-friendly graphical user interface and interfaces for customizing or integrating various feature extraction algorithms, thereby significantly reducing barriers for nonexperts. It holds the promise of significantly accelerating data production in UAV phenotyping experiments.
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Affiliation(s)
- Botao Wang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan, Hubei 430070, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, Hubei 430070, China
| | - Chenghai Yang
- Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USA
| | - Jian Zhang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan, Hubei 430070, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, Hubei 430070, China
| | - Yunhao You
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan, Hubei 430070, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, Hubei 430070, China
| | - Hongming Wang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, 1 Shizishan Street, Wuhan, Hubei 430070, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, Hubei 430070, China
| | - Wanneng Yang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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18
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Zhang Y, Wu X, Wang X, Dai M, Peng Y. Crop root system architecture in drought response. J Genet Genomics 2024:S1673-8527(24)00100-0. [PMID: 38723744 DOI: 10.1016/j.jgg.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 07/27/2024]
Abstract
Drought is a natural disaster that profoundly impacts on global agricultural production, significantly reduces crop yields, and thereby poses a severe threat to worldwide food security. Addressing the challenge of effectively improving crop drought resistance (DR) to mitigate yield loss under drought conditions is a global issue. An optimal root system architecture (RSA) plays a pivotal role in enhancing crops' capacity to efficiently uptake water and nutrients, which consequently strengthens their resilience against environmental stresses. In this review, we discuss the compositions and roles of crop RSA and summarize the most recent developments in augmenting drought tolerance in crops by manipulating RSA-related genes. Based on the current research, we propose the potential optimal RSA configuration that could be helpful in enhancing crop DR. Lastly, we discussed the existing challenges and future directions for breeding crops with enhanced DR capabilities through genetic improvements targeting RSA.
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Affiliation(s)
- Yanjun Zhang
- College of Agronomy, Gansu Agricultural University, Lanzhou, Gansu 730070, China; State Key Laboratory of Aridland Crop Science, Gansu Agricultural University, Lanzhou, Gansu 730070, China; Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu 730070, China; Key Laboratory of Crop Gene Resources and Germplasm Innovation in Northwest Cold and Arid Regions, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu 730070, China
| | - Xi Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, HuBei 430070, China
| | - Xingrong Wang
- Crop Research Institute, Gansu Academy of Agricultural Sciences, Lanzhou, Gansu 730070, China; Key Laboratory of Crop Gene Resources and Germplasm Innovation in Northwest Cold and Arid Regions, Ministry of Agriculture and Rural Affairs, Lanzhou, Gansu 730070, China
| | - Mingqiu Dai
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei 430070, China; Hubei Hongshan Laboratory, Wuhan, HuBei 430070, China.
| | - Yunling Peng
- College of Agronomy, Gansu Agricultural University, Lanzhou, Gansu 730070, China; State Key Laboratory of Aridland Crop Science, Gansu Agricultural University, Lanzhou, Gansu 730070, China.
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19
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Yu R, Cao X, Liu J, Nie R, Zhang C, Yuan M, Huang Y, Liu X, Zheng W, Wang C, Wu T, Su B, Kang Z, Zeng Q, Han D, Wu J. Using UAV-Based Temporal Spectral Indices to Dissect Changes in the Stay-Green Trait in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0171. [PMID: 38694449 PMCID: PMC11062509 DOI: 10.34133/plantphenomics.0171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/17/2024] [Indexed: 05/04/2024]
Abstract
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100, TracesCS6B03G0356400, and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
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Affiliation(s)
- Rui Yu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaofeng Cao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jia Liu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Ruiqi Nie
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chuanliang Zhang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Meng Yuan
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yanchuan Huang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xinzhe Liu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weijun Zheng
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Changfa Wang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Tingting Wu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Baofeng Su
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Qingdong Zeng
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dejun Han
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jianhui Wu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
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20
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Das Choudhury S, Guadagno CR, Bashyam S, Mazis A, Ewers BE, Samal A, Awada T. Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning. FRONTIERS IN PLANT SCIENCE 2024; 15:1353110. [PMID: 38708393 PMCID: PMC11066247 DOI: 10.3389/fpls.2024.1353110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 03/14/2024] [Indexed: 05/07/2024]
Abstract
Background Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.
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Affiliation(s)
- Sruti Das Choudhury
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
| | | | - Srinidhi Bashyam
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Anastasios Mazis
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Brent E. Ewers
- Department of Botany, University of Wyoming, Laramie, WY, United States
| | - Ashok Samal
- School of Computing, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Tala Awada
- School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, United States
- Agricultural Research Division, University of Nebraska-Lincoln, Lincoln, NE, United States
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21
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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22
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Berrigan EM, Wang L, Carrillo H, Echegoyen K, Kappes M, Torres J, Ai-Perreira A, McCoy E, Shane E, Copeland CD, Ragel L, Georgousakis C, Lee S, Reynolds D, Talgo A, Gonzalez J, Zhang L, Rajurkar AB, Ruiz M, Daniels E, Maree L, Pariyar S, Busch W, Pereira TD. Fast and Efficient Root Phenotyping via Pose Estimation. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0175. [PMID: 38629082 PMCID: PMC11020144 DOI: 10.34133/plantphenomics.0175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/20/2024] [Indexed: 04/19/2024]
Abstract
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wolfgang Busch
- Salk Institute for Biological Studies, La Jolla, CA 92037, USA
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23
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Leiva F, Dhakal R, Himanen K, Ortiz R, Chawade A. The Combination of Low-Cost, Red-Green-Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch. PLANTS (BASEL, SWITZERLAND) 2024; 13:1039. [PMID: 38611568 PMCID: PMC11013667 DOI: 10.3390/plants13071039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/26/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.
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Affiliation(s)
- Fernanda Leiva
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, Sweden; (F.L.); (R.O.)
| | - Rishap Dhakal
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, 1575 Linden Dr, Madison, WI 53706, USA
| | - Kristiina Himanen
- National Plant Phenotyping Infrastructure, Helsinki Institute of Life Science, Biocenter Finland, University of Helsinki, Latokartanonkaari 7, 00790 Helsinki, Finland;
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, Sweden; (F.L.); (R.O.)
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), P.O. Box 190, SE-23422 Lomma, Sweden; (F.L.); (R.O.)
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24
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Wang X, Li X, Zhao W, Hou X, Dong S. Current views of drought research: experimental methods, adaptation mechanisms and regulatory strategies. FRONTIERS IN PLANT SCIENCE 2024; 15:1371895. [PMID: 38638344 PMCID: PMC11024477 DOI: 10.3389/fpls.2024.1371895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 03/20/2024] [Indexed: 04/20/2024]
Abstract
Drought stress is one of the most important abiotic stresses which causes many yield losses every year. This paper presents a comprehensive review of recent advances in international drought research. First, the main types of drought stress and the commonly used drought stress methods in the current experiment were introduced, and the advantages and disadvantages of each method were evaluated. Second, the response of plants to drought stress was reviewed from the aspects of morphology, physiology, biochemistry and molecular progression. Then, the potential methods to improve drought resistance and recent emerging technologies were introduced. Finally, the current research dilemma and future development direction were summarized. In summary, this review provides insights into drought stress research from different perspectives and provides a theoretical reference for scholars engaged in and about to engage in drought research.
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Affiliation(s)
- Xiyue Wang
- College of Agriculture, Northeast Agricultural University, Heilongjiang, Harbin, China
| | - Xiaomei Li
- College of Agriculture, Heilongjiang Agricultural Engineering Vocational College, Heilongjiang, Harbin, China
| | - Wei Zhao
- College of Agriculture, Northeast Agricultural University, Heilongjiang, Harbin, China
| | - Xiaomin Hou
- Millet Research Institute, Qiqihar Sub-Academy of Heilongjiang Academy of Agricultural Sciences, Heilongjiang, Qiqihar, China
| | - Shoukun Dong
- College of Agriculture, Northeast Agricultural University, Heilongjiang, Harbin, China
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25
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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26
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Yu H, Weng L, Wu S, He J, Yuan Y, Wang J, Xu X, Feng X. Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0158. [PMID: 38524738 PMCID: PMC10959008 DOI: 10.34133/plantphenomics.0158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 02/21/2024] [Indexed: 03/26/2024]
Abstract
The rate of soybean canopy establishment largely determines photoperiodic sensitivity, subsequently influencing yield potential. However, assessing the rate of soybean canopy development in large-scale field breeding trials is both laborious and time-consuming. High-throughput phenotyping methods based on unmanned aerial vehicle (UAV) systems can be used to monitor and quantitatively describe the development of soybean canopies for different genotypes. In this study, high-resolution and time-series raw data from field soybean populations were collected using UAVs. The RGB (red, green, and blue) and infrared images are used as inputs to construct the multimodal image segmentation model-the RGB & Infrared Feature Fusion Segmentation Network (RIFSeg-Net). Subsequently, the segment anything model was employed to extract complete individual leaves from the segmentation results obtained from RIFSeg-Net. These leaf aspect ratios facilitated the accurate categorization of soybean populations into 2 distinct varieties: oval leaf type variety and lanceolate leaf type variety. Finally, dynamic modeling was conducted to identify 5 phenotypic traits associated with the canopy development rate that differed substantially among the classified soybean varieties. The results showed that the developed multimodal image segmentation model RIFSeg-Net for extracting soybean canopy cover from UAV images outperformed traditional deep learning image segmentation networks (precision = 0.94, recall = 0.93, F1-score = 0.93). The proposed method has high practical value in the field of germplasm resource identification. This approach could lead to the use of a practical tool for further genotypic differentiation analysis and the selection of target genes.
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Affiliation(s)
- Hui Yu
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Lin Weng
- Zhejiang Lab, Hangzhou 310012, China
| | | | | | | | - Jun Wang
- Zhejiang Lab, Hangzhou 310012, China
| | | | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology,
Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
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27
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Wu Y, Wen W, Gu S, Huang G, Wang C, Lu X, Xiao P, Guo X, Huang L. Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0160. [PMID: 38510827 PMCID: PMC10950926 DOI: 10.34133/plantphenomics.0160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/26/2024] [Indexed: 03/22/2024]
Abstract
The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 104 plants ha-1. This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.
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Affiliation(s)
- Yandong Wu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application,
Anhui University, Hefei 230601, China
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Weiliang Wen
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Nongxin Science & Technology (Beijing) Co., Ltd, Beijing 100097, China
| | - Shenghao Gu
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Guanmin Huang
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Nongxin Science & Technology (Beijing) Co., Ltd, Beijing 100097, China
| | - Chuanyu Wang
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xianju Lu
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Nongxin Science & Technology (Beijing) Co., Ltd, Beijing 100097, China
| | - Pengliang Xiao
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application,
Anhui University, Hefei 230601, China
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xinyu Guo
- Information Technology Research Center,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application,
Anhui University, Hefei 230601, China
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28
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Raza A, Chen H, Zhang C, Zhuang Y, Sharif Y, Cai T, Yang Q, Soni P, Pandey MK, Varshney RK, Zhuang W. Designing future peanut: the power of genomics-assisted breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:66. [PMID: 38438591 DOI: 10.1007/s00122-024-04575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 02/03/2024] [Indexed: 03/06/2024]
Abstract
KEY MESSAGE Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.
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Affiliation(s)
- Ali Raza
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Hua Chen
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Chong Zhang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yuhui Zhuang
- College of Life Science, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yasir Sharif
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Tiecheng Cai
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Qiang Yang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Pooja Soni
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Manish K Pandey
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Rajeev K Varshney
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia.
| | - Weijian Zhuang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China.
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29
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Tan Z, Han X, Dai C, Lu S, He H, Yao X, Chen P, Yang C, Zhao L, Yang QY, Zou J, Wen J, Hong D, Liu C, Ge X, Fan C, Yi B, Zhang C, Ma C, Liu K, Shen J, Tu J, Yang G, Fu T, Guo L, Zhao H. Functional genomics of Brassica napus: Progresses, challenges, and perspectives. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2024; 66:484-509. [PMID: 38456625 DOI: 10.1111/jipb.13635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/19/2024] [Indexed: 03/09/2024]
Abstract
Brassica napus, commonly known as rapeseed or canola, is a major oil crop contributing over 13% to the stable supply of edible vegetable oil worldwide. Identification and understanding the gene functions in the B. napus genome is crucial for genomic breeding. A group of genes controlling agronomic traits have been successfully cloned through functional genomics studies in B. napus. In this review, we present an overview of the progress made in the functional genomics of B. napus, including the availability of germplasm resources, omics databases and cloned functional genes. Based on the current progress, we also highlight the main challenges and perspectives in this field. The advances in the functional genomics of B. napus contribute to a better understanding of the genetic basis underlying the complex agronomic traits in B. napus and will expedite the breeding of high quality, high resistance and high yield in B. napus varieties.
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Affiliation(s)
- Zengdong Tan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Xu Han
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Cheng Dai
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shaoping Lu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Hanzi He
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xuan Yao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Peng Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chao Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lun Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qing-Yong Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Jun Zou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jing Wen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Dengfeng Hong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Chao Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianhong Ge
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chuchuan Fan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Bing Yi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chunyu Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chaozhi Ma
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinxiong Shen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jinxing Tu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Guangsheng Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Tingdong Fu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572025, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
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Ji X, Zhou Z, Gouda M, Zhang W, He Y, Ye G, Li X. A novel labor-free method for isolating crop leaf pixels from RGB imagery: Generating labels via a topological strategy. COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024; 218:108631. [DOI: 10.1016/j.compag.2024.108631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
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31
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Pugh NA, Young A, Ojha M, Emendack Y, Sanchez J, Xin Z, Puppala N. Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms. FRONTIERS IN PLANT SCIENCE 2024; 15:1339864. [PMID: 38444530 PMCID: PMC10912196 DOI: 10.3389/fpls.2024.1339864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024]
Abstract
Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.
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Affiliation(s)
- N. Ace Pugh
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Andrew Young
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Manisha Ojha
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
| | - Yves Emendack
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Jacobo Sanchez
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Zhanguo Xin
- United States Department of Agriculture, Crop Stress Research Laboratory, Lubbock, TX, United States
| | - Naveen Puppala
- Agricultural Science Center at Clovis, New Mexico State University, Clovis, NM, United States
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Sarkar S, Ganapathysubramanian B, Singh A, Fotouhi F, Kar S, Nagasubramanian K, Chowdhary G, Das SK, Kantor G, Krishnamurthy A, Merchant N, Singh AK. Cyber-agricultural systems for crop breeding and sustainable production. TRENDS IN PLANT SCIENCE 2024; 29:130-149. [PMID: 37648631 DOI: 10.1016/j.tplants.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/19/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023]
Abstract
The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.
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Affiliation(s)
- Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA.
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Fateme Fotouhi
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA
| | | | | | - Girish Chowdhary
- Department of Agricultural and Biological Engineering and Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, Urbana, IL, USA
| | - Sajal K Das
- Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA
| | - George Kantor
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, AZ, USA
| | - Asheesh K Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA.
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Keller-Przybylkowicz S, Oskiera M, Liu X, Song L, Zhao L, Du X, Kruczynska D, Walencik A, Kowara N, Bartoszewski G. Transcriptome Analysis of White- and Red-Fleshed Apple Fruits Uncovered Novel Genes Related to the Regulation of Anthocyanin Biosynthesis. Int J Mol Sci 2024; 25:1778. [PMID: 38339057 PMCID: PMC10855924 DOI: 10.3390/ijms25031778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
The red flesh coloration of apples is a result of a biochemical pathway involved in the biosynthesis of anthocyanins and anthocyanidins. Based on apple genome analysis, a high number of regulatory genes, mainly transcription factors such as MYB, which are components of regulatory complex MYB-bHLH-WD40, and several structural genes (PAL, 4CL, CHS, CHI, F3H, DFR, ANS, UFGT) involved in anthocyanin biosynthesis, have been identified. In this study, we investigated novel genes related to the red-flesh apple phenotype. These genes could be deemed molecular markers for the early selection of new apple cultivars. Based on a comparative transcriptome analysis of apples with different fruit-flesh coloration, we successfully identified and characterized ten potential genes from the plant hormone transduction pathway of auxin (GH3); cytokinins (B-ARR); gibberellins (DELLA); abscisic acid (SnRK2 and ABF); brassinosteroids (BRI1, BZR1 and TCH4); jasmonic acid (MYC2); and salicylic acid (NPR1). An analysis of expression profiles was performed in immature and ripe fruits of red-fleshed cultivars. We have uncovered genes mediating the regulation of abscisic acid, salicylic acid, cytokinin, and jasmonic acid signaling and described their role in anthocyanin biosynthesis, accumulation, and degradation. The presented results underline the relationship between genes from the hormone signal transduction pathway and UFGT genes, which are directly responsible for anthocyanin color transformation as well as anthocyanin accumulation during apple-fruit ripening.
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Affiliation(s)
- Sylwia Keller-Przybylkowicz
- The National Institute of Horticultural Research, Konstytucji 3-go Maja, 96-100 Skierniewice, Poland; (M.O.); (A.W.); (N.K.)
| | - Michal Oskiera
- The National Institute of Horticultural Research, Konstytucji 3-go Maja, 96-100 Skierniewice, Poland; (M.O.); (A.W.); (N.K.)
| | - Xueqing Liu
- Yantai Academy of Agricultural Science, Gangechengxida Street No 26, Fushan District, Yantai 265500, China; (X.L.); (L.Z.); (X.D.)
| | - Laiqing Song
- Yantai Academy of Agricultural Science, Gangechengxida Street No 26, Fushan District, Yantai 265500, China; (X.L.); (L.Z.); (X.D.)
| | - Lingling Zhao
- Yantai Academy of Agricultural Science, Gangechengxida Street No 26, Fushan District, Yantai 265500, China; (X.L.); (L.Z.); (X.D.)
| | - Xiaoyun Du
- Yantai Academy of Agricultural Science, Gangechengxida Street No 26, Fushan District, Yantai 265500, China; (X.L.); (L.Z.); (X.D.)
| | - Dorota Kruczynska
- The National Institute of Horticultural Research, Konstytucji 3-go Maja, 96-100 Skierniewice, Poland; (M.O.); (A.W.); (N.K.)
| | - Agnieszka Walencik
- The National Institute of Horticultural Research, Konstytucji 3-go Maja, 96-100 Skierniewice, Poland; (M.O.); (A.W.); (N.K.)
| | - Norbert Kowara
- The National Institute of Horticultural Research, Konstytucji 3-go Maja, 96-100 Skierniewice, Poland; (M.O.); (A.W.); (N.K.)
| | - Grzegorz Bartoszewski
- Department of Plant Genetics Breeding and Biotechnology, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland;
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Zhang J, Kaiser E, Marcelis LFM, Vialet-Chabrand S. Rapid spatial assessment of leaf-absorbed irradiance. THE NEW PHYTOLOGIST 2024; 241:1866-1876. [PMID: 38124293 DOI: 10.1111/nph.19496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
Image-based high-throughput phenotyping promises the rapid determination of functional traits in large plant populations. However, interpretation of some traits - such as those related to photosynthesis or transpiration rates - is only meaningful if the irradiance absorbed by the measured leaves is known, which can differ greatly between different parts of the same plant and within canopies. No feasible method currently exists to rapidly measure absorbed irradiance in three-dimensional plants and canopies. We developed a method and protocols to derive absorbed irradiance at any visible part of a canopy with a thermal camera, by fitting a leaf energy balance model to transient changes in leaf temperature. Leaves were exposed to short light pulses (30 s) that were not long enough to trigger stomatal opening but strong enough to induce transient changes in leaf temperature that was proportional to the absorbed irradiance. The method was successfully validated against point measurements of absorbed irradiance in plant species with relatively simple architecture (sweet pepper, cucumber, tomato, and lettuce). Once calibrated, the model was used to produce absorbed irradiance maps from thermograms. Our method opens new avenues for the interpretation of plant responses derived from imaging techniques and can be adapted to existing high-throughput phenotyping platforms.
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Affiliation(s)
- Jiayu Zhang
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands
| | - Elias Kaiser
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands
| | - Leo F M Marcelis
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands
| | - Silvere Vialet-Chabrand
- Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, 6708 PB, Wageningen, the Netherlands
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Knoch D, Meyer RC, Heuermann MC, Riewe D, Peleke FF, Szymański J, Abbadi A, Snowdon RJ, Altmann T. Integrated multi-omics analyses and genome-wide association studies reveal prime candidate genes of metabolic and vegetative growth variation in canola. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:713-728. [PMID: 37964699 DOI: 10.1111/tpj.16524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023]
Abstract
Genome-wide association studies (GWAS) identified thousands of genetic loci associated with complex plant traits, including many traits of agronomical importance. However, functional interpretation of GWAS results remains challenging because of large candidate regions due to linkage disequilibrium. High-throughput omics technologies, such as genomics, transcriptomics, proteomics and metabolomics open new avenues for integrative systems biological analyses and help to nominate systems information supported (prime) candidate genes. In the present study, we capitalise on a diverse canola population with 477 spring-type lines which was previously analysed by high-throughput phenotyping of growth-related traits and by RNA sequencing and metabolite profiling for multi-omics-based hybrid performance prediction. We deepened the phenotypic data analysis, now providing 123 time-resolved image-based traits, to gain insight into the complex relations during early vegetative growth and reanalysed the transcriptome data based on the latest Darmor-bzh v10 genome assembly. Genome-wide association testing revealed 61 298 robust quantitative trait loci (QTL) including 187 metabolite QTL, 56814 expression QTL and 4297 phenotypic QTL, many clustered in pronounced hotspots. Combining information about QTL colocalisation across omics layers and correlations between omics features allowed us to discover prime candidate genes for metabolic and vegetative growth variation. Prioritised candidate genes for early biomass accumulation include A06p05760.1_BnaDAR (PIAL1), A10p16280.1_BnaDAR, C07p48260.1_BnaDAR (PRL1) and C07p48510.1_BnaDAR (CLPR4). Moreover, we observed unequal effects of the Brassica A and C subgenomes on early biomass production.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Rhonda C Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Marc C Heuermann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
- Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, 14195, Berlin, Germany
| | - Fritz F Peleke
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
| | - Jędrzej Szymański
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
- Institute of Bio- and Geosciences IBG-4: Bioinformatics, Forschungszentrum Jülich, 52428, Jülich, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363, Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363, Holtsee, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, Research Centre for Biosystems, Land Use and Nutrition (iFZ), Justus-Liebig-University Giessen, 35392, Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Corrensstrasse 3, Seeland OT, Gatersleben, Germany
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Garegnani M, Sandri C, Pacelli C, Ferranti F, Bennici E, Desiderio A, Nardi L, Villani ME. Non-destructive real-time analysis of plant metabolite accumulation in radish microgreens under different LED light recipes. FRONTIERS IN PLANT SCIENCE 2024; 14:1289208. [PMID: 38273958 PMCID: PMC10808373 DOI: 10.3389/fpls.2023.1289208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/29/2023] [Indexed: 01/27/2024]
Abstract
Introduction The future of human space missions relies on the ability to provide adequate food resources for astronauts and also to reduce stress due to the environment (microgravity and cosmic radiation). In this context, microgreens have been proposed for the astronaut diet because of their fast-growing time and their high levels of bioactive compounds and nutrients (vitamins, antioxidants, minerals, etc.), which are even higher than mature plants, and are usually consumed as ready-to-eat vegetables. Methods Our study aimed to identify the best light recipe for the soilless cultivation of two cultivars of radish microgreens (Raphanus sativus, green daikon, and rioja improved) harvested eight days after sowing that could be used for space farming. The effects on plant metabolism of three different light emitting diodes (LED) light recipes (L1-20% red, 20% green, 60% blue; L2-40% red, 20% green, 40% blue; L3-60% red, 20% green, 20% blue) were tested on radish microgreens hydroponically grown. A fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism. The adopted sensors allowed us to quantitatively estimate the fluorescence of flavonols, anthocyanins, and chlorophyll via specific indices verified by standardized spectrophotometric methods. To assess plant growth, morphometric parameters (fresh and dry weight, cotyledon area and weight, hypocotyl length) were analyzed. Results We observed a statistically significant positive effect on biomass accumulation and productivity for both cultivars grown under the same light recipe (40% blue, 20% green, 40% red). We further investigated how the addition of UV and/or far-red LED lights could have a positive effect on plant metabolite accumulation (anthocyanins and flavonols). Discussion These results can help design plant-based bioregenerative life-support systems for long-duration human space exploration, by integrating fluorescence-based non-destructive techniques to monitor the accumulation of metabolites with nutraceutical properties in soilless cultivated microgreens.
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Affiliation(s)
- Marco Garegnani
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
- Department of Aerospace Science and Technology, Politecnico of Milano, Milan, Italy
| | - Carla Sandri
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
| | - Claudia Pacelli
- Human Spaceflight and Scientific Research Unit, Italian Space Agency, Rome, Italy
| | - Francesca Ferranti
- Human Spaceflight and Scientific Research Unit, Italian Space Agency, Rome, Italy
| | - Elisabetta Bennici
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
| | - Angiola Desiderio
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
| | - Luca Nardi
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
| | - Maria Elena Villani
- ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Department for Sustainability Casaccia Research Center, Rome, Italy
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Yang W, Feng H, Hu X, Song J, Guo J, Lu B. An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management. Methods Mol Biol 2024; 2787:3-38. [PMID: 38656479 DOI: 10.1007/978-1-0716-3778-4_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In this chapter, we explore the application of high-throughput crop phenotyping facilities for phenotype data acquisition and the extraction of significant information from the collected data through image processing and data mining methods. Additionally, the construction and outlook of crop phenotype databases are introduced and the need for global cooperation and data sharing is emphasized. High-throughput crop phenotyping significantly improves accuracy and efficiency compared to traditional measurements, making significant contributions to overcoming bottlenecks in the phenotyping field and advancing crop genetics.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiao Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jingyan Song
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jing Guo
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Bingjie Lu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
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Ma L, Deng D, Su Y, Xiao L. X-ray-μCT: nondestructively identifying hidden microphenotypes inside living crop seeds. TRENDS IN PLANT SCIENCE 2024; 29:99-100. [PMID: 37973441 DOI: 10.1016/j.tplants.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/20/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Liying Ma
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Danyi Deng
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China
| | - Yi Su
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China.
| | - Langtao Xiao
- College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410128, China.
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Tang C, Jiang B, Ejaz I, Ameen A, Zhang R, Mo X, Wang Z. High-throughput phenotyping of nutritional quality components in sweet potato roots by near-infrared spectroscopy and chemometrics methods. Food Chem X 2023; 20:100916. [PMID: 38144853 PMCID: PMC10739761 DOI: 10.1016/j.fochx.2023.100916] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 09/18/2023] [Accepted: 09/30/2023] [Indexed: 12/26/2023] Open
Abstract
The lack of an efficient approach for quality evaluation of sweet potatoes significantly hinders progress in quality breeding. Therefore, this study aimed to establish a near-infrared spectroscopy (NIRS) assay for high-throughput analysis of sweet potato root quality, including total starch, amylose, amylopectin, the ratio of amylopectin to amylose, soluble sugar, crude protein, total flavonoid content, and total phenolic content. A total of 125 representative samples were utilized and a dual-optimized strategy (optimization of sample subset partitioning and variable selection) was applied to NIRS modeling. Eight optimal equations were developed with an excellent coefficient of determination for the calibration (R2C) at 0.95-0.99, cross-validation (R2CV) at 0.93-0.98, external validation (R2V) at 0.89-0.96, and the ratio of prediction to deviation (RPD) at 6.33-11.35. Overall, these NIRS models provide a feasible approach for high-throughput analysis of root quality and permit large-scale screening of elite germplasm in future sweet potato breeding.
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Affiliation(s)
- Chaochen Tang
- Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou 510640, People's Republic of China
| | - Bingzhi Jiang
- Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou 510640, People's Republic of China
| | - Irsa Ejaz
- College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, People's Republic of China
| | - Asif Ameen
- Arid Zone Research Centre, Pakistan Agricultural Research Council, Dera Ismail Khan, Pakistan
| | - Rong Zhang
- Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou 510640, People's Republic of China
| | - Xueying Mo
- Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou 510640, People's Republic of China
| | - Zhangying Wang
- Crops Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Crop Genetic Improvement of Guangdong Province, Guangzhou 510640, People's Republic of China
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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Singer WM, Lee YC, Shea Z, Vieira CC, Lee D, Li X, Cunicelli M, Kadam SS, Khan MAW, Shannon G, Mian MAR, Nguyen HT, Zhang B. Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed. THE PLANT GENOME 2023; 16:e20415. [PMID: 38084377 DOI: 10.1002/tpg2.20415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 12/22/2023]
Abstract
Soybean [Glycine max (L.) Merr.] is a globally important crop due to its valuable seed composition, versatile feed, food, and industrial end-uses, and consistent genetic gain. Successful genetic gain in soybean has led to widespread adaptation and increased value for producers, processors, and consumers. Specific focus on the nutritional quality of soybean seed composition for food and feed has further elucidated genetic knowledge and bolstered breeding progress. Seed components are historical and current targets for soybean breeders seeking to improve nutritional quality of soybean. This article reviews genetic and genomic foundations for improvement of nutritionally important traits, such as protein and amino acids, oil and fatty acids, carbohydrates, and specific food-grade considerations; discusses the application of advanced breeding technology such as CRISPR/Cas9 in creating seed composition variations; and provides future directions and breeding recommendations regarding soybean seed composition traits.
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Affiliation(s)
- William M Singer
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Yi-Chen Lee
- Department of Agriculture, Fort Hays State University, Hays, Kansas, USA
| | - Zachary Shea
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Caio Canella Vieira
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, Arkansas, USA
| | - Dongho Lee
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - Xiaoying Li
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Mia Cunicelli
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Shaila S Kadam
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | | | - Grover Shannon
- Fisher Delta Research, Extension, and Education Center, University of Missouri, Portageville, Missouri, USA
| | - M A Rouf Mian
- Soybean and Nitrogen Fixation Research Unit, USDA-ARS, Raleigh, North Carolina, USA
| | - Henry T Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, USA
| | - Bo Zhang
- School of Plant and Environmental Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
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42
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Li R, Chen S, Matsumoto H, Gouda M, Gafforov Y, Wang M, Liu Y. Predicting rice diseases using advanced technologies at different scales: present status and future perspectives. ABIOTECH 2023; 4:359-371. [PMID: 38106429 PMCID: PMC10721578 DOI: 10.1007/s42994-023-00126-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/30/2023] [Indexed: 12/19/2023]
Abstract
The past few years have witnessed significant progress in emerging disease detection techniques for accurately and rapidly tracking rice diseases and predicting potential solutions. In this review we focus on image processing techniques using machine learning (ML) and deep learning (DL) models related to multi-scale rice diseases. Furthermore, we summarize applications of different detection techniques, including genomic, physiological, and biochemical approaches. In addition, we also present the state-of-the-art in contemporary optical sensing applications of pathogen-plant interaction phenotypes. This review serves as a valuable resource for researchers seeking effective solutions to address the challenges of high-throughput data and model recognition for early detection of issues affecting rice crops through ML and DL models.
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Affiliation(s)
- Ruyue Li
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058 China
| | - Sishi Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
| | - Haruna Matsumoto
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
| | - Mostafa Gouda
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
- Department of Nutrition and Food Science, National Research Centre, Giza, 12622 Egypt
| | - Yusufjon Gafforov
- Central Asian Center for Development Studies, New Uzbekistan University, Tashkent, 100000 Uzbekistan
| | - Mengcen Wang
- State Key Laboratory of Rice Biology, and Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou, 310058 China
- Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo, 060-8589 Japan
| | - Yufei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058 China
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Swartz LG, Liu S, Dahlquist D, Kramer ST, Walter ES, McInturf SA, Bucksch A, Mendoza-Cózatl DG. OPEN leaf: an open-source cloud-based phenotyping system for tracking dynamic changes at leaf-specific resolution in Arabidopsis. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 116:1600-1616. [PMID: 37733751 DOI: 10.1111/tpj.16449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 08/16/2023] [Indexed: 09/23/2023]
Abstract
The first draft of the Arabidopsis genome was released more than 20 years ago and despite intensive molecular research, more than 30% of Arabidopsis genes remained uncharacterized or without an assigned function. This is in part due to gene redundancy within gene families or the essential nature of genes, where their deletion results in lethality (i.e., the dark genome). High-throughput plant phenotyping (HTPP) offers an automated and unbiased approach to characterize subtle or transient phenotypes resulting from gene redundancy or inducible gene silencing; however, access to commercial HTPP platforms remains limited. Here we describe the design and implementation of OPEN leaf, an open-source phenotyping system with cloud connectivity and remote bilateral communication to facilitate data collection, sharing and processing. OPEN leaf, coupled with our SMART imaging processing pipeline was able to consistently document and quantify dynamic changes at the whole rosette level and leaf-specific resolution when plants experienced changes in nutrient availability. Our data also demonstrate that VIS sensors remain underutilized and can be used in high-throughput screens to identify and characterize previously unidentified phenotypes in a leaf-specific time-dependent manner. Moreover, the modular and open-source design of OPEN leaf allows seamless integration of additional sensors based on users and experimental needs.
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Affiliation(s)
- Landon G Swartz
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Suxing Liu
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - Drew Dahlquist
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
| | - Skyler T Kramer
- MU Institute of Data Science and Informatics, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollinst St., Columbia, Missouri, 65211, USA
| | - Emily S Walter
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Samuel A McInturf
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
| | - Alexander Bucksch
- School of Plant Sciences, University of Arizona, 1140 E South Campus, Tucson, Arizona, 85721, USA
| | - David G Mendoza-Cózatl
- Department of Electrical Engineering and Computer Science, University of Missouri, 411 S 6th St., Columbia, Missouri, 65201, USA
- Division of Plant Science and Technology, C.S. Bond Life Sciences Center, University of Missouri, 1201 Rollins St., Columbia, Missouri, 65211, USA
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Wang X, Huang J, Peng S, Xiong D. Leaf rolling precedes stomatal closure in rice (Oryza sativa) under drought conditions. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:6650-6661. [PMID: 37551729 DOI: 10.1093/jxb/erad316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 08/05/2023] [Indexed: 08/09/2023]
Abstract
Leaf rolling is a physiological response to drought that may help to reduce water loss, but its significance as a contribution to drought tolerance is uncertain. We scored the leaf rolling of four rice genotypes along an experimental drought gradient using an improved cryo-microscopy method. Leaf water potential (Ψleaf), gas exchange, chlorophyll fluorescence, leaf hydraulic conductance, rehydration capacity, and the bulk turgor loss point were also analysed. During the drought process, stomatal conductance declined sharply to reduce water loss, and leaves rolled up before the stomata completely closed. The leaf water loss rate of rolled leaves was significantly reduced compared with artificially flattened leaves. The Ψleaf threshold of initial leaf rolling ranged from -1.95 to -1.04 MPa across genotypes. When a leaf rolled so that the leaf edges were touching, photosynthetic rate and stomatal conductance declined more than 80%. Across genotypes, leaf hydraulic conductance declined first, followed by gas exchange and chlorophyll fluorescence parameters. However, the Ψleaf threshold for a given functional trait decline differed significantly among genotypes, with the exception of leaf hydraulic conductance. Our results suggested that leaf rolling was mechanistically linked to drought avoidance and tolerance traits and might serve as a useful phenotypic trait for rice breeding in future drought scenarios.
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Affiliation(s)
- Xiaoxiao Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jianliang Huang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Shaobing Peng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Dongliang Xiong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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Berrigan EM, Wang L, Carrillo H, Echegoyen K, Kappes M, Torres J, Ai-Perreira A, McCoy E, Shane E, Copeland CD, Ragel L, Georgousakis C, Lee S, Reynolds D, Talgo A, Gonzalez J, Zhang L, Rajurkar AB, Ruiz M, Daniels E, Maree L, Pariyar S, Busch W, Pereira TD. Fast and efficient root phenotyping via pose estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567949. [PMID: 38045278 PMCID: PMC10690188 DOI: 10.1101/2023.11.20.567949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
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Affiliation(s)
| | - Lin Wang
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Hannah Carrillo
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Kimberly Echegoyen
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Mikayla Kappes
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Jorge Torres
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Angel Ai-Perreira
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Erica McCoy
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Emily Shane
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Charles D. Copeland
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Lauren Ragel
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | | | - Sanghwa Lee
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Dawn Reynolds
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Avery Talgo
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Juan Gonzalez
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Ling Zhang
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Ashish B. Rajurkar
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Michel Ruiz
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Erin Daniels
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Liezl Maree
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Shree Pariyar
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Wolfgang Busch
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Talmo D. Pereira
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
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Stejskal J, Čepl J, Neuwirthová E, Akinyemi OO, Chuchlík J, Provazník D, Keinänen M, Campbell P, Albrechtová J, Lstibůrek M, Lhotáková Z. Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0111. [PMID: 38026471 PMCID: PMC10644830 DOI: 10.34133/plantphenomics.0111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023]
Abstract
Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.
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Affiliation(s)
- Jan Stejskal
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Jaroslav Čepl
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Eva Neuwirthová
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Olusegun Olaitan Akinyemi
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Jiří Chuchlík
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Daniel Provazník
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Markku Keinänen
- Department of Environmental and Biological Sciences,
University of Eastern Finland, Joensuu, Finland
- Center for Photonic Sciences,
University of Eastern Finland, Joensuu, Finland
| | - Petya Campbell
- Department of Geography and Environmental Sciences,
University of Maryland Baltimore County, Baltimore, MD, USA
- Biospheric Sciences Laboratory,
NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jana Albrechtová
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
| | - Milan Lstibůrek
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences,
Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Zuzana Lhotáková
- Department of Experimental Plant Biology,
Charles University, Prague, Czech Republic
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47
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Barreto Ortiz J, Hirsch CN, Ehlke NJ, Watkins E. SpykProps: an imaging pipeline to quantify architecture in unilateral grass inflorescences. PLANT METHODS 2023; 19:125. [PMID: 37957737 PMCID: PMC10644492 DOI: 10.1186/s13007-023-01104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Inflorescence properties such length, spikelet number, and their spatial distribution across the rachis, are fundamental indicators of seed productivity in grasses and have been a target of selection throughout domestication and crop improvement. However, quantifying such complex morphology is laborious, time-consuming, and commonly limited to human-perceived traits. These limitations can be exacerbated by unfavorable trait correlations between inflorescence architecture and seed yield that can be unconsciously selected for. Computer vision offers an alternative to conventional phenotyping, enabling higher throughput and reducing subjectivity. These approaches provide valuable insights into the determinants of seed yield, and thus, aid breeding decisions. RESULTS Here, we described SpykProps, an inexpensive Python-based imaging system to quantify morphological properties in unilateral inflorescences, that was developed and tested on images of perennial grass (Lolium perenne L.) spikes. SpykProps is able to rapidly and accurately identify spikes (RMSE < 1), estimate their length (R2 = 0.96), and number of spikelets (R2 = 0.61). It also quantifies color and shape from hundreds of interacting descriptors that are accurate predictors of architectural and agronomic traits such as seed yield potential (R2 = 0.94), rachis weight (R2 = 0.83), and seed shattering (R2 = 0.85). CONCLUSIONS SpykProps is an open-source platform to characterize inflorescence architecture in a wide range of grasses. This imaging tool generates conventional and latent traits that can be used to better characterize developmental and agronomic traits associated with inflorescence architecture, and has applications in fields that include breeding, physiology, evolution, and development biology.
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Affiliation(s)
- Joan Barreto Ortiz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Nancy Jo Ehlke
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric Watkins
- Department of Horticultural Science, University of Minnesota, St. Paul, MN, 55108, USA.
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48
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Safdar LB, Foulkes MJ, Kleiner FH, Searle IR, Bhosale RA, Fisk ID, Boden SA. Challenges facing sustainable protein production: Opportunities for cereals. PLANT COMMUNICATIONS 2023; 4:100716. [PMID: 37710958 PMCID: PMC10721536 DOI: 10.1016/j.xplc.2023.100716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/01/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
Rising demands for protein worldwide are likely to drive increases in livestock production, as meat provides ∼40% of dietary protein. This will come at a significant environmental cost, and a shift toward plant-based protein sources would therefore provide major benefits. While legumes provide substantial amounts of plant-based protein, cereals are the major constituents of global foods, with wheat alone accounting for 15-20% of the required dietary protein intake. Improvement of protein content in wheat is limited by phenotyping challenges, lack of genetic potential of modern germplasms, negative yield trade-offs, and environmental costs of nitrogen fertilizers. Presenting wheat as a case study, we discuss how increasing protein content in cereals through a revised breeding strategy combined with robust phenotyping could ensure a sustainable protein supply while minimizing the environmental impact of nitrogen fertilizer.
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Affiliation(s)
- Luqman B Safdar
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA 5064, Australia; School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - M John Foulkes
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Friedrich H Kleiner
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK; Faculty of Applied Science, Kavli Institute of Nanoscience, Delft University of Technology, van der Maasweg 9, 2629 HZ Delft, the Netherlands
| | - Iain R Searle
- School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
| | - Rahul A Bhosale
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK
| | - Ian D Fisk
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA 5064, Australia; School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough LE12 5RD, UK.
| | - Scott A Boden
- School of Agriculture, Food and Wine, Waite Research Institute, University of Adelaide, Glen Osmond, SA 5064, Australia.
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Sharma N, Raman H, Wheeler D, Kalenahalli Y, Sharma R. Data-driven approaches to improve water-use efficiency and drought resistance in crop plants. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 336:111852. [PMID: 37659733 DOI: 10.1016/j.plantsci.2023.111852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.
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Affiliation(s)
- Niharika Sharma
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.
| | - Harsh Raman
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - David Wheeler
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia
| | - Yogendra Kalenahalli
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana 502324, India
| | - Rita Sharma
- Department of Biological Sciences, BITS Pilani, Pilani Campus, Rajasthan 333031, India
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Blasch G, Anberbir T, Negash T, Tilahun L, Belayineh FY, Alemayehu Y, Mamo G, Hodson DP, Rodrigues FA. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Sci Rep 2023; 13:16768. [PMID: 37798287 PMCID: PMC10556098 DOI: 10.1038/s41598-023-43770-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/28/2023] [Indexed: 10/07/2023] Open
Abstract
Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.
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Affiliation(s)
- Gerald Blasch
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia.
| | - Tadesse Anberbir
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Tamirat Negash
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | - Lidiya Tilahun
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | | | - Yoseph Alemayehu
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia
| | - Girma Mamo
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - David P Hodson
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francelino A Rodrigues
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Lincoln Agritech Ltd, Lincoln University, Lincoln, New Zealand
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