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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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2
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Špundová M, Kučerová Z, Nožková V, Opatíková M, Procházková L, Klimeš P, Nauš J. What to Choose for Estimating Leaf Water Status-Spectral Reflectance or In vivo Chlorophyll Fluorescence? PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0243. [PMID: 39211292 PMCID: PMC11358408 DOI: 10.34133/plantphenomics.0243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
In the context of global climate change and the increasing need to study plant response to drought, there is a demand for easily, rapidly, and remotely measurable parameters that sensitively reflect leaf water status. Parameters with this potential include those derived from leaf spectral reflectance (R) and chlorophyll fluorescence. As each of these methods probes completely different leaf characteristics, their sensitivity to water loss may differ in different plant species and/or under different circumstances, making it difficult to choose the most appropriate method for estimating water status in a given situation. Here, we present a simple comparative analysis to facilitate this choice for leaf-level measurements. Using desiccation of tobacco (Nicotiana tabacum L. cv. Samsun) and barley (Hordeum vulgare L. cv. Bojos) leaves as a model case, we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients (coefficient of reliability, sensitivity, and inaccuracy). This comparison showed that, in our case, chlorophyll fluorescence was more reliable and universal than spectral R. Nevertheless, it is most appropriate to use both methods simultaneously, as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.
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Affiliation(s)
- Martina Špundová
- Department of Biophysics, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
| | - Zuzana Kučerová
- Department of Biophysics, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
| | - Vladimíra Nožková
- Department of Chemical Biology, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
| | - Monika Opatíková
- Department of Biophysics, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
| | - Lucie Procházková
- Department of Biophysics, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
| | - Pavel Klimeš
- Czech Advanced Technology and Research Institute (CATRIN), Palacký University Olomouc, Šlechtitelů 27, Olomouc, 783 71, Czech Republic
| | - Jan Nauš
- Department of Biophysics, Faculty of Science,
Palacký University, Šlechtitelů 27, Olomouc 783 71, Czech Republic
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3
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Zhou L, Zhang H, Bian L, Tian Y, Zhou H. Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0205. [PMID: 39077119 PMCID: PMC11283870 DOI: 10.34133/plantphenomics.0205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/05/2024] [Indexed: 07/31/2024]
Abstract
Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.
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Affiliation(s)
- Lei Zhou
- College of Mechanical and Electronic Engineering,
Nanjing Forestry University, Nanjing 210037, P. R. China
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources,
Nanjing Forestry University, Nanjing 210037, P. R. China
| | - Huichun Zhang
- College of Mechanical and Electronic Engineering,
Nanjing Forestry University, Nanjing 210037, P. R. China
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources,
Nanjing Forestry University, Nanjing 210037, P. R. China
| | - Liming Bian
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Forest Genetics & Biotechnology of Ministry of Education, Nanjing Forestry University, Nanjing 210037, P. R. China
| | - Ye Tian
- College of Forestry and Grassland,
Nanjing Forestry University, Nanjing 210037, P. R. China
| | - Haopeng Zhou
- College of Mechanical and Electronic Engineering,
Nanjing Forestry University, Nanjing 210037, P. R. China
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4
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Wang J, Chu Y, Chen G, Zhao M, Wu J, Qu R, Wang Z. Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0197. [PMID: 39049839 PMCID: PMC11266478 DOI: 10.34133/plantphenomics.0197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 05/16/2024] [Indexed: 07/27/2024]
Abstract
Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.
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Affiliation(s)
- Jinfeng Wang
- College of Engineering,
Northeast Agricultural University, Harbin 150000, China
| | - Yuhang Chu
- College of Engineering,
Northeast Agricultural University, Harbin 150000, China
| | - Guoqing Chen
- College of Engineering,
Northeast Agricultural University, Harbin 150000, China
| | - Minyi Zhao
- College of Engineering,
Northeast Agricultural University, Harbin 150000, China
| | - Jizhuang Wu
- Yantai Agricultural Technology Popularization Center, Yantai 261400, China
| | - Ritao Qu
- Yantai Agricultural Technology Popularization Center, Yantai 261400, China
| | - Zhentao Wang
- College of Engineering,
Northeast Agricultural University, Harbin 150000, China
- College of Life Sciences,
Northwest A&F University, Yangling 712100, China
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5
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Zheng H, Tang W, Yang T, Zhou M, Guo C, Cheng T, Cao W, Zhu Y, Zhang Y, Yao X. Grain Protein Content Phenotyping in Rice via Hyperspectral Imaging Technology and a Genome-Wide Association Study. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0200. [PMID: 38978968 PMCID: PMC11227985 DOI: 10.34133/plantphenomics.0200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/18/2024] [Indexed: 07/10/2024]
Abstract
Efficient and accurate acquisition of the rice grain protein content (GPC) is important for selecting high-quality rice varieties, and remote sensing technology is an attractive potential method for this task. However, the majority of multispectral sensors are poor predictors of GPC due to their broad spectral bands. Hyperspectral technology provides a new analytical technology for bridging the gap between phenomics and genomics. However, the small size of typical datasets is a constraint for model construction for estimating GPC, limiting their accuracy and reducing their ability to generalize to a wide range of varieties. In this study, we used hyperspectral data of rice grains from 515 japonica varieties and deep convolution generative adversarial networks (DCGANs) to generate simulated data to improve the model accuracy. Features sensitive to GPC were extracted after applying a continuous wavelet transform (CWT), and the estimated GPC model was constructed by partial least squares regression (PLSR). Finally, a genome-wide association study (GWAS) was applied to the measured and generated datasets to detect GPC loci. The results demonstrated that the simulated GPC values generated after 8,000 epochs were closest to the measured values. The wavelet feature (WF1743, 2), obtained from the data with the addition of 200 simulated samples, exhibited the highest GPC estimation accuracy (R 2 = 0.58 and RRMSE = 6.70%). The GWAS analysis showed that the estimated values based on the simulated data detected the same loci as the measured values, including the OsmtSSB1L gene related to grain storage protein. This study provides a new technique for the efficient genetic study of phenotypic traits in rice based on hyperspectral technology.
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Affiliation(s)
- Hengbiao Zheng
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
- Zhongshan Biological Breeding Laboratory,Nanjing, China
| | - Weijie Tang
- Zhongshan Biological Breeding Laboratory,Nanjing, China
- Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology,
Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
| | - Tao Yang
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Meng Zhou
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Caili Guo
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Tao Cheng
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Weixing Cao
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Yan Zhu
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Yunhui Zhang
- Zhongshan Biological Breeding Laboratory,Nanjing, China
- Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology,
Jiangsu Academy of Agricultural Sciences, Nanjing, Jiangsu, China
| | - Xia Yao
- National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory of Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu, China
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6
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Gepts P. Biocultural diversity and crop improvement. Emerg Top Life Sci 2023; 7:151-196. [PMID: 38084755 PMCID: PMC10754339 DOI: 10.1042/etls20230067] [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: 09/27/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023]
Abstract
Biocultural diversity is the ever-evolving and irreplaceable sum total of all living organisms inhabiting the Earth. It plays a significant role in sustainable productivity and ecosystem services that benefit humanity and is closely allied with human cultural diversity. Despite its essentiality, biodiversity is seriously threatened by the insatiable and inequitable human exploitation of the Earth's resources. One of the benefits of biodiversity is its utilization in crop improvement, including cropping improvement (agronomic cultivation practices) and genetic improvement (plant breeding). Crop improvement has tended to decrease agricultural biodiversity since the origins of agriculture, but awareness of this situation can reverse this negative trend. Cropping improvement can strive to use more diverse cultivars and a broader complement of crops on farms and in landscapes. It can also focus on underutilized crops, including legumes. Genetic improvement can access a broader range of biodiversity sources and, with the assistance of modern breeding tools like genomics, can facilitate the introduction of additional characteristics that improve yield, mitigate environmental stresses, and restore, at least partially, lost crop biodiversity. The current legal framework covering biodiversity includes national intellectual property and international treaty instruments, which have tended to limit access and innovation to biodiversity. A global system of access and benefit sharing, encompassing digital sequence information, would benefit humanity but remains an elusive goal. The Kunming-Montréal Global Biodiversity Framework sets forth an ambitious set of targets and goals to be accomplished by 2030 and 2050, respectively, to protect and restore biocultural diversity, including agrobiodiversity.
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Affiliation(s)
- Paul Gepts
- Department of Plant Sciences, Section of Crop and Ecosystem Sciences, University of California, Davis, CA 95616-8780, U.S.A
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7
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Gómez-Candón D, Bellvert J, Pelechá A, Lopes MS. A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation. PLANTS (BASEL, SWITZERLAND) 2023; 12:3871. [PMID: 38005768 PMCID: PMC10675030 DOI: 10.3390/plants12223871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023]
Abstract
This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.
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Affiliation(s)
- David Gómez-Candón
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Joaquim Bellvert
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Ana Pelechá
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Marta S. Lopes
- Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain;
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8
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Hu H, Xu Z, Wei Y, Wang T, Zhao Y, Xu H, Mao X, Huang L. The Identification of Fritillaria Species Using Hyperspectral Imaging with Enhanced One-Dimensional Convolutional Neural Networks via Attention Mechanism. Foods 2023; 12:4153. [PMID: 38002210 PMCID: PMC10670081 DOI: 10.3390/foods12224153] [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: 09/06/2023] [Revised: 10/14/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
Combining deep learning and hyperspectral imaging (HSI) has proven to be an effective approach in the quality control of medicinal and edible plants. Nonetheless, hyperspectral data contains redundant information and highly correlated characteristic bands, which can adversely impact sample identification. To address this issue, we proposed an enhanced one-dimensional convolutional neural network (1DCNN) with an attention mechanism. Given an intermediate feature map, two attention modules are constructed along two separate dimensions, channel and spectral, and then combined to enhance relevant features and to suppress irrelevant ones. Validated by Fritillaria datasets, the results demonstrate that an attention-enhanced 1DCNN model outperforms several machine learning algorithms and shows consistent improvements over a vanilla 1DCNN. Notably under VNIR and SWIR lenses, the model obtained 98.97% and 99.35% for binary classification between Fritillariae Cirrhosae Bulbus (FCB) and other non-FCB species, respectively. Additionally, it still achieved an extraordinary accuracy of 97.64% and 98.39% for eight-category classification among Fritillaria species. This study demonstrated the application of HSI with artificial intelligence can serve as a reliable, efficient, and non-destructive quality control method for authenticating Fritillaria species. Moreover, our findings also illustrated the great potential of the attention mechanism in enhancing the performance of the vanilla 1DCNN method, providing reference for other HSI-related quality controls of plants with medicinal and edible uses.
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Affiliation(s)
- Huiqiang Hu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Zhenyu Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yunpeng Wei
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Tingting Wang
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Yuping Zhao
- China Academy of Chinese Medical Sciences, Beijing 100070, China
| | - Huaxing Xu
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Xiaobo Mao
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Luqi Huang
- China Academy of Chinese Medical Sciences, Beijing 100070, China
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9
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
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