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Concepcion JS, Noble AD, Thompson AM, Dong Y, Olson EL. Genomic regions influencing the hyperspectral phenome of deoxynivalenol infected wheat. Sci Rep 2024; 14:19340. [PMID: 39164367 PMCID: PMC11336138 DOI: 10.1038/s41598-024-69830-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 08/09/2024] [Indexed: 08/22/2024] Open
Abstract
The quantitative nature of fusarium head blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identified regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397-673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. The first principal component (PC1) of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in a genome wide association study and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.
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Affiliation(s)
- Jonathan S Concepcion
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Amanda D Noble
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Addie M Thompson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA
| | - Yanhong Dong
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, 55108, USA
| | - Eric L Olson
- Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI, 48823, USA.
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2
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Sharma A, Hazarika M, Heisnam P, Pandey H, Devadas VASN, Kesavan AK, Kumar P, Singh D, Vashishth A, Jha R, Misra V, Kumar R. Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding. ACS OMEGA 2024; 9:29114-29138. [PMID: 39005787 PMCID: PMC11238293 DOI: 10.1021/acsomega.3c09060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/25/2024] [Accepted: 05/31/2024] [Indexed: 07/16/2024]
Abstract
The controlled environment ecosystem is a meticulously designed plant growing chamber utilized for cultivating biofortified crops and microgreens, addressing hidden hunger and malnutrition prevalent in the growing population. The integration of speed breeding within such controlled environments effectively eradicates morphological disruptions encountered in traditional breeding methods such as inbreeding depression, male sterility, self-incompatibility, embryo abortion, and other unsuccessful attempts. In contrast to the unpredictable climate conditions that often prolong breeding cycles to 10-15 years in traditional breeding and 4-5 years in transgenic breeding within open ecosystems, speed breeding techniques expedite the achievement of breeding objectives and F1-F6 generations within 2-3 years under controlled growing conditions. In comparison, traditional breeding may take 5-10 years for plant population line creation, 3-5 years for field trials, and 1-2 years for variety release. The effectiveness of speed breeding in trait improvement and population line development varies across different crops, requiring approximately 4 generations in rice and groundnut, 5 generations in soybean, pea, and oat, 6 generations in sorghum, Amaranthus sp., and subterranean clover, 6-7 generations in bread wheat, durum wheat, and chickpea, 7 generations in broad bean, 8 generations in lentil, and 10 generations in Arabidopsis thaliana annually within controlled environment ecosystems. Artificial intelligence leverages neural networks and algorithm models to screen phenotypic traits and assess their role in diverse crop species. Moreover, in controlled environment systems, mechanistic models combined with machine learning effectively regulate stable nutrient use efficiency, water use efficiency, photosynthetic assimilation product, metabolic use efficiency, climatic factors, greenhouse gas emissions, carbon sequestration, and carbon footprints. However, any negligence, even minor, in maintaining optimal photoperiodism, temperature, humidity, and controlling pests or diseases can lead to the deterioration of crop trials and speed breeding techniques within the controlled environment system. Further comparative studies are imperative to comprehend and justify the efficacy of climate management techniques in controlled environment ecosystems compared to natural environments, with or without soil.
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Affiliation(s)
- Avinash Sharma
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Mainu Hazarika
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Punabati Heisnam
- College of Agriculture, Central Agricultural University, Iroisemba, Manipur 795004, India
| | - Himanshu Pandey
- PG Department of Agriculture, Khalsa College, Amritsar, Punjab 143002, India
| | | | - Ajith Kumar Kesavan
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Praveen Kumar
- Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan 342304, India
| | - Devendra Singh
- Faculty of Biotechnology, Shri Ramswaroop Memorial University, Barabanki, Uttar Pradesh 225003, India
| | - Amit Vashishth
- Patanjali Herbal Research Department, Patanjali Research Institute, Haridwar, Uttarakhand 249405, India
| | - Rani Jha
- ISBM University, Gariyaband, Chhattishgarh 493996, India
| | - Varucha Misra
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow, Uttar Pradesh 226002, India
| | - Rajeev Kumar
- Division of Plant Physiology and Biochemistry, ICAR-Indian Institute of Sugarcane Research, Lucknow, Uttar Pradesh 226002, India
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3
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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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4
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Jeong SW, Lyu JI, Jeong H, Baek J, Moon JK, Lee C, Choi MG, Kim KH, Park YI. SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits. PLANT CELL REPORTS 2024; 43:164. [PMID: 38852113 PMCID: PMC11162974 DOI: 10.1007/s00299-024-03249-0] [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: 04/03/2024] [Accepted: 05/06/2024] [Indexed: 06/10/2024]
Abstract
KEY MESSAGE Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis.
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Affiliation(s)
- Seok Won Jeong
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea
| | - Jae Il Lyu
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - HwangWeon Jeong
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jeongho Baek
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Jung-Kyung Moon
- Crop Foundation Research Division, National Institute of Crop Sciences, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Chaewon Lee
- Crop Cultivation and Environment Research Division, National Institute of Crop Sciences, 54 Seohoro, Suwon, Kyounggi-do, 16613, Korea
| | - Myoung-Goo Choi
- Wheat Research Team, National Institute of Crop Sciences, RDA, 181 Hyeoksinro, Wanju, Jeollabuk-do, 55365, Korea
| | - Kyoung-Hwan Kim
- Gene Engineering Division, National Institute of Agricultural Sciences, 370 Nongsaengmyeongro, Jeonju, Jeollabuk-do, 54874, Korea
| | - Youn-Il Park
- Biological Sciences, Chungnam National University, 99 Daehagro, Youseong, Daejon, 34134, Korea.
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5
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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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6
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Luo S, Yuan X, Liang R, Feng K, Xu H, Zhao J, Wang S, Lan Y, Long Y, Deng H. Prediction and visualization of gene modulated ultralow cadmium accumulation in brown rice grains by hyperspectral imaging. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 297:122720. [PMID: 37058840 DOI: 10.1016/j.saa.2023.122720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 05/14/2023]
Abstract
Monitoring (including prediction and visualization) the gene modulated cadmium (Cd) accumulation in rice grains is one of the most important steps for identification of key transporter genes responsible for grain Cd accumulation and breeding low grain-Cd-accumulating rice cultivars. A method to predict and visualize the gene modulated ultralow Cd accumulation in brown rice grains based on the hyperspectral image (HSI) technology is proposed in this study. Firstly, the Vis-NIR HSIs of brown rice grain samples with 48Cd content levels induced by gene modulation (ranging from 0.0637 to 0.1845 mg/kg) are collected using HSI system. Then, Kernel-ridge (KRR) and random forest (RFR) regression models based on full spectral data and the data after feature dimension reduction (FDR) with kernel principal component analysis (KPCA) and truncated singular value decomposition (TSVD) algorithms are established to predict the Cd contents. RFR model shows poor performance due to the over-fitting based on the full spectral data, while the KRR model can obtain a good predict accuracy with Rp2 of 0.9035, RMSEP of 0.0037 and RPD of 3.278. After the FDR of the full spectral data, the RFR model combined with TSVD reaches the optimum prediction accuracy with Rp2 of 0.9056, RMSEP of 0.0074 and RPD of 3.318, and the best prediction precision of KRR model can also be further enhanced by TSVD with Rp2 of 0.9224, RMSEP of 0.0067 and RPD of 3.512. Finally, the visualization of the predicted Cd accumulation in brown rice grains are realized based on the best regression model (KRR + TSVD). The results of this work indicate that Vis-NIR HSI has great potential for detection and visualization gene modulation induced ultralow Cd accumulation and transport in rice crops.
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Affiliation(s)
- Shuiyang Luo
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Xue Yuan
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
| | - Ruiqing Liang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Kunsheng Feng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haitao Xu
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Jing Zhao
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Shaokui Wang
- Guangdong Provincial Key Laboratory of Plant Molecular Breeding, South China Agricultural University, Guangzhou 510642, China
| | - Yubin Lan
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Yongbing Long
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
| | - Haidong Deng
- College of Electronic Engineering / College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China.
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7
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Safdar LB, Dugina K, Saeidan A, Yoshicawa GV, Caporaso N, Gapare B, Umer MJ, Bhosale RA, Searle IR, Foulkes MJ, Boden SA, Fisk ID. Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging. Food Energy Secur 2023; 12:e498. [PMID: 38440412 PMCID: PMC10909436 DOI: 10.1002/fes3.498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 03/06/2024] Open
Abstract
A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.
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Affiliation(s)
- Luqman B. Safdar
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Kateryna Dugina
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Ali Saeidan
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Guilherme V. Yoshicawa
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | | | - Brighton Gapare
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - M. Jawad Umer
- Cotton Research InstituteChinese Academy of Agricultural SciencesAnyangChina
| | - Rahul A. Bhosale
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Iain R. Searle
- School of Biological SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Scott A. Boden
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Ian D. Fisk
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
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8
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Song B, Ning W, Wei D, Jiang M, Zhu K, Wang X, Edwards D, Odeny DA, Cheng S. Plant genome resequencing and population genomics: Current status and future prospects. MOLECULAR PLANT 2023; 16:1252-1268. [PMID: 37501370 DOI: 10.1016/j.molp.2023.07.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 05/30/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023]
Abstract
Advances in DNA sequencing technology have sparked a genomics revolution, driving breakthroughs in plant genetics and crop breeding. Recently, the focus has shifted from cataloging genetic diversity in plants to exploring their functional significance and delivering beneficial alleles for crop improvement. This transformation has been facilitated by the increasing adoption of whole-genome resequencing. In this review, we summarize the current progress of population-based genome resequencing studies and how these studies affect crop breeding. A total of 187 land plants from 163 countries have been resequenced, comprising 54 413 accessions. As part of resequencing efforts 367 traits have been surveyed and 86 genome-wide association studies have been conducted. Economically important crops, particularly cereals, vegetables, and legumes, have dominated the resequencing efforts, leaving a gap in 49 orders, including Lycopodiales, Liliales, Acorales, Austrobaileyales, and Commelinales. The resequenced germplasm is distributed across diverse geographic locations, providing a global perspective on plant genomics. We highlight genes that have been selected during domestication, or associated with agronomic traits, and form a repository of candidate genes for future research and application. Despite the opportunities for cross-species comparative genomics, many population genomic datasets are not accessible, impeding secondary analyses. We call for a more open and collaborative approach to population genomics that promotes data sharing and encourages contribution-based credit policy. The number of plant genome resequencing studies will continue to rise with the decreasing DNA sequencing costs, coupled with advances in analysis and computational technologies. This expansion, in terms of both scale and quality, holds promise for deeper insights into plant trait genetics and breeding design.
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Affiliation(s)
- Bo Song
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Weidong Ning
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; Huazhong Agricultural University, College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Wuhan, Hubei, China
| | - Di Wei
- Biotechnology Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 53007, China
| | - Mengyun Jiang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Kun Zhu
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - Xingwei Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China; State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng 475004, China; Shenzhen Research Institute of Henan University, Shenzhen 518000, China
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Damaris A Odeny
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) - Eastern and Southern Africa, Nairobi, Kenya
| | - Shifeng Cheng
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
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9
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Li W, Lin M, Li J, Liu D, Tan W, Yin X, Zhai Y, Zhou Y, Xing W. Genome-wide association study of drought tolerance traits in sugar beet germplasms at the seedling stage. Front Genet 2023; 14:1198600. [PMID: 37547461 PMCID: PMC10401439 DOI: 10.3389/fgene.2023.1198600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/30/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction: Sugar beets are an important crop for global sugar production. Intense drought and the increasing lack of water resources pose a great threat to sugar beet cultivation. It is a priority to investigate favourable germplasms and functional genes to improve the breeding of drought tolerant plants. Methods: Thus, in this study, 328 sugar beet germplasms were used in a genome-wide association study (GWAS) to identify single nucleotide polymorphism (SNP) markers and candidate genes associated with drought tolerance. Results: The results showed that under drought stress (9% PEG-6000), there were 11 significantly associated loci on chromosomes 2, 3, 5, 7, and 9 from the 108946 SNPs filtered using a mixed linear model (MLM). Genome-wide association analysis combined with qRT-PCR identified 13 genes that were significantly differentially expressed in drought-tolerant extreme materials. Discussion: These candidate genes mainly exhibited functions such as regulating sugar metabolism, maintaining internal environmental stability and participating in photosystem repair. This study provides valuable information for exploring the molecular mechanisms of drought tolerance and improvement in sugar beet.
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Affiliation(s)
- Wangsheng Li
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Ming Lin
- Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Jiajia Li
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Dali Liu
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Wenbo Tan
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Xilong Yin
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yan Zhai
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
| | - Yuanhang Zhou
- Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Wang Xing
- National Beet Medium-Term Gene Bank, Heilongjiang University, Harbin, China
- Key Laboratory of Sugar Beet Genetics and Breeding, College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, China
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10
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Reyes VP. Fantastic genes: where and how to find them? Exploiting rice genetic resources for the improvement of yield, tolerance, and resistance to a wide array of stresses in rice. Funct Integr Genomics 2023; 23:238. [PMID: 37439874 DOI: 10.1007/s10142-023-01159-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 07/14/2023]
Abstract
Rice production is a critical component of global food security. To date, rice is grown in over 100 countries and is the primary source of food for more than 3 billion people. Despite its importance, rice production is facing numerous challenges that threaten its future viability. One of the primary problems is the advent of climate change. The changing climatic conditions greatly affect the growth and productivity of rice crop and the quality of rice yield. Similarly, biotic stresses brought about by pathogen and pest infestations are greatly affecting the productivity of rice. To address these issues, the utilization of rice genetic resources is necessary to map, identify, and understand the genetics of important agronomic traits. This review paper highlights the role of rice genetic resources for developing high-yielding and stress-tolerant rice varieties. The integration of genetic, genomic, and phenomic tools in rice breeding programs has led to the development of high-yielding and stress-tolerant rice varieties. The collaboration of multidisciplinary teams of experts, sustainable farming practices, and extension services for farmers is essential for accelerating the development of high-yielding and stress-tolerant rice varieties.
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11
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Massahiro Yassue R, Galli G, James Chen C, Fritsche‐Neto R, Morota G. Genome-wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth-related traits in maize under plant growth-promoting bacteria inoculation. PLANT DIRECT 2023; 7:e492. [PMID: 37102161 PMCID: PMC10123960 DOI: 10.1002/pld3.492] [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: 08/10/2022] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Plant growth-promoting bacteria (PGPB) may be of use for increasing crop yield and plant resilience to biotic and abiotic stressors. Using hyperspectral reflectance data to assess growth-related traits may shed light on the underlying genetics as such data can help assess biochemical and physiological traits. This study aimed to integrate hyperspectral reflectance data with genome-wide association analyses to examine maize growth-related traits under PGPB inoculation. A total of 360 inbred maize lines with 13,826 single nucleotide polymorphisms (SNPs) were evaluated with and without PGPB inoculation; 150 hyperspectral wavelength reflectances at 386-1021 nm and 131 hyperspectral indices were used in the analysis. Plant height, stalk diameter, and shoot dry mass were measured manually. Overall, hyperspectral signatures produced similar or higher genomic heritability estimates than those of manually measured phenotypes, and they were genetically correlated with manually measured phenotypes. Furthermore, several hyperspectral reflectance values and spectral indices were identified by genome-wide association analysis as potential markers for growth-related traits under PGPB inoculation. Eight SNPs were detected, which were commonly associated with manually measured and hyperspectral phenotypes. Different genomic regions were found for plant growth and hyperspectral phenotypes between with and without PGPB inoculation. Moreover, the hyperspectral phenotypes were associated with genes previously reported as candidates for nitrogen uptake efficiency, tolerance to abiotic stressors, and kernel size. In addition, a Shiny web application was developed to explore multiphenotype genome-wide association results interactively. Taken together, our results demonstrate the usefulness of hyperspectral-based phenotyping for studying maize growth-related traits in response to PGPB inoculation.
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Affiliation(s)
- Rafael Massahiro Yassue
- Department of Genetics, ‘Luiz de Queiroz’ College of AgricultureUniversity of São PauloSão PauloBrazil
- School of Animal SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
| | - Giovanni Galli
- Department of Genetics, ‘Luiz de Queiroz’ College of AgricultureUniversity of São PauloSão PauloBrazil
| | - Chun‐Peng James Chen
- School of Animal SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
- Center for Advanced Innovation in AgricultureVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
| | - Roberto Fritsche‐Neto
- Department of Genetics, ‘Luiz de Queiroz’ College of AgricultureUniversity of São PauloSão PauloBrazil
- Quantitative Genetics and Biometrics ClusterInternational Rice Research InstituteLos BañosPhilippines
| | - Gota Morota
- School of Animal SciencesVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
- Center for Advanced Innovation in AgricultureVirginia Polytechnic Institute and State UniversityBlacksburgVirginiaUSA
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12
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Che S, Du G, Zhong X, Mo Z, Wang Z, Mao Y. Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0012. [PMID: 37040513 PMCID: PMC10076050 DOI: 10.34133/plantphenomics.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 06/19/2023]
Abstract
Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE (R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC (R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC (R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.
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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Xuefeng Zhong
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhendong Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572002, China
- Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, 572025, China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266073, China
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13
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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14
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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15
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Jha UC, Nayyar H, Parida SK, Deshmukh R, von Wettberg EJB, Siddique KHM. Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools. Int J Mol Sci 2022; 23:7710. [PMID: 35887057 PMCID: PMC9325250 DOI: 10.3390/ijms23147710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.
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Affiliation(s)
- Uday C. Jha
- ICAR—Indian Institute of Pulses Research (IIPR), Kanpur 208024, India
| | - Harsh Nayyar
- Department of Botany, Panjab University, Chandigarh 160014, India;
| | - Swarup K. Parida
- National Institute of Plant Genome Research, New Delhi 110067, India;
| | - Rupesh Deshmukh
- National Agri-Food Biotechnology Institute, Punjab 140308, India;
| | | | - Kadambot H. M. Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001, Australia
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16
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Nansen C, Imtiaz MS, Mesgaran MB, Lee H. Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects. PLANT METHODS 2022; 18:74. [PMID: 35658997 PMCID: PMC9164469 DOI: 10.1186/s13007-022-00912-z] [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: 12/14/2021] [Accepted: 05/22/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.
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Affiliation(s)
- Christian Nansen
- Department of Entomology and Nematology, University of California, Davis, USA.
- Department of Entomology and Nematology, UC Davis Briggs Hall, Room 367, Davis, CA, 95616, USA.
| | - Mohammad S Imtiaz
- Department of Electrical & Computer Engineering, Bradley University, Peoria, USA
| | | | - Hyoseok Lee
- Department of Entomology and Nematology, University of California, Davis, USA
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17
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Jiang C, Rashid MAR, Zhang Y, Zhao Y, Pan Y. Genome wide association study on development and evolution of glutinous rice. BMC Genom Data 2022; 23:33. [PMID: 35508973 PMCID: PMC9066796 DOI: 10.1186/s12863-022-01033-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Glutinous rice as a special endosperm type is consumed as a staple food in East Asian countries by consumers' preference. Genetic studies on glutinous rice could be conducive to improve rice quality and understand its development and evolution. Therefor, we sought to explore more genes related to glutinous by genome wide association study and research the formation history for glutinous. RESULTS Here, genome-wide association study was performed to explore the associated loci/genes underlying glutinous rice by using 2108 rice accessions. Combining the expression patterns analysis, 127, 81, and 48 candidate genes were identified to be associated with endosperm type in whole rice panel, indica, and japonica sub-populations. There were 32 genes, including three starch synthesis-related genes Wx, SSG6, and OsSSIIa, detected simultaneously in the whole rice panel and subpopulations, playing important role in determining glutinous rice. The combined haplotype analyses revealed that the waxy haplotypes combination of three genes mainly distributed in Southeast Asia (SEA), SEA islands (SER) and East Asia islands (EAR). Through population structure and genetic differentiation, we suggest that waxy haplotypes of the three genes firstly evolved or were directly inherited from wild rice in japonica, and then introgressed into indica in SER, SEA and EAR. CONCLUSIONS The cloning and natural variation analysis of waxy-related genes are of great significance for the genetic improvement of quality breeding and comprehend the history in glutinous rice. This work provides valuable information for further gene discovery and understanding the evolution and formation for glutinous rice in SEA, SER and EAR.
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Affiliation(s)
- Conghui Jiang
- Shandong Rice Engineering Technology Research Center, Shandong Rice Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Muhammad Abdul Rehman Rashid
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, 38000, Pakistan.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Research Center of Perennial Rice Engineering and Technology in Yunnan, School of Agriculture, Yunnan University, Kunming, 650500, China
| | - Yanhong Zhang
- Institute of Nuclear and Biological Technologies, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, China
| | - Yan Zhao
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong, 271018, PR China.
| | - Yinghua Pan
- Rice Research Institute, Guangxi Academy of Agricultural Sciences/Guangxi Key Laboratory of Rice Genetics and Breeding, Nanning, 530007, China.
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18
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Pinit S, Ruengchaijatuporn N, Sriswasdi S, Buaboocha T, Chadchawan S, Chaiwanon J. Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice. PLoS One 2022; 17:e0267304. [PMID: 35443012 PMCID: PMC9020724 DOI: 10.1371/journal.pone.0267304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/06/2022] [Indexed: 11/25/2022] Open
Abstract
Phosphorus (P) is an essential mineral nutrient and one of the key factors determining crop productivity. P-deficient plants exhibit visual leaf symptoms, including chlorosis, and alter spectral reflectance properties. In this study, we evaluated leaf inorganic phosphate (Pi) contents, plant growth and reflectance spectra (420–790 nm) of 172 Thai rice landrace varieties grown hydroponically under three different P supplies (overly sufficient, mildly deficient and severely deficient conditions). We reported correlations between Pi contents and reflectance ratios computed from two wavebands in the range of near infrared (720–790 nm) and visible energy (green-yellow and red edge) (r > 0.69) in Pi-deficient leaves. Artificial neural network models were also developed which could classify P deficiency levels with 85.60% accuracy and predict Pi content with R2 of 0.53, as well as highlight important waveband sections. Using 217 reflectance ratio indices to perform genome-wide association study (GWAS) with 113,114 SNPs, we identified 11 loci associated with the spectral reflectance traits, some of which were also associated with the leaf Pi content trait. Hyperspectral measurement offers a promising non-destructive approach to predict plant P status and screen large germplasm for varieties with high P use efficiency.
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Affiliation(s)
- Sompop Pinit
- Faculty of Science, Department of Botany, Center of Excellence in Environment and Plant Physiology, Chulalongkorn University, Bangkok, Thailand
- Faculty of Science, Program in Biotechnology, Chulalongkorn University, Bangkok, Thailand
| | | | - Sira Sriswasdi
- Faculty of Medicine, Computational Molecular Biology Group, Chulalongkorn University, Bangkok, Thailand
- Faculty of Medicine, Research Affairs, Chulalongkorn University, Bangkok, Thailand
| | - Teerapong Buaboocha
- Faculty of Science, Omics Sciences and Bioinformatics Center, Chulalongkorn University, Bangkok, Thailand
- Faculty of Science, Department of Biochemistry, Molecular Crop Research Unit, Chulalongkorn University, Bangkok, Thailand
| | - Supachitra Chadchawan
- Faculty of Science, Department of Botany, Center of Excellence in Environment and Plant Physiology, Chulalongkorn University, Bangkok, Thailand
| | - Juthamas Chaiwanon
- Faculty of Science, Department of Botany, Center of Excellence in Environment and Plant Physiology, Chulalongkorn University, Bangkok, Thailand
- * E-mail:
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19
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Sarić R, Nguyen VD, Burge T, Berkowitz O, Trtílek M, Whelan J, Lewsey MG, Čustović E. Applications of hyperspectral imaging in plant phenotyping. TRENDS IN PLANT SCIENCE 2022; 27:301-315. [PMID: 34998690 DOI: 10.1016/j.tplants.2021.12.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/30/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.
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Affiliation(s)
- Rijad Sarić
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Viet D Nguyen
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Timothy Burge
- Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Oliver Berkowitz
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Martin Trtílek
- Photon Systems Instruments plant phenotyping research centre, Photon System Instruments, 664 24 Drasov, Brno, Czech Republic
| | - James Whelan
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia.
| | - Mathew G Lewsey
- Department of Animal, Plant and Soil Science, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia; Australian Research Council Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Edhem Čustović
- Department of Engineering, School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
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20
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Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
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Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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21
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Xiao Q, Bai X, Zhang C, He Y. Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review. J Adv Res 2022; 35:215-230. [PMID: 35003802 PMCID: PMC8721248 DOI: 10.1016/j.jare.2021.05.002] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 05/05/2021] [Accepted: 05/09/2021] [Indexed: 01/22/2023] Open
Abstract
Linking phenotypes and genotypes to identify genetic architectures that regulate important traits is crucial for plant breeding and the development of plant genomics. In recent years, genome-wide association studies (GWASs) have been applied extensively to interpret relationships between genes and traits. Successful GWAS application requires comprehensive genomic and phenotypic data from large populations. Although multiple high-throughput DNA sequencing approaches are available for the generation of genomics data, the capacity to generate high-quality phenotypic data is lagging far behind. Traditional methods for plant phenotyping mostly rely on manual measurements, which are laborious, inaccurate, and time-consuming, greatly impairing the acquisition of phenotypic data from large populations. In contrast, high-throughput phenotyping has unique advantages, facilitating rapid, non-destructive, and high-throughput detection, and, in turn, addressing the shortcomings of traditional methods. Aim of Review: This review summarizes the current status with regard to the integration of high-throughput phenotyping and GWAS in plants, in addition to discussing the inherent challenges and future prospects. Key Scientific Concepts of Review: High-throughput phenotyping, which facilitates non-contact and dynamic measurements, has the potential to offer high-quality trait data for GWAS and, in turn, to enhance the unraveling of genetic structures of complex plant traits. In conclusion, high-throughput phenotyping integration with GWAS could facilitate the revealing of coding information in plant genomes.
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Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Xiulin Bai
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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22
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Sethy PK, Pandey C, Sahu YK, Behera SK. Hyperspectral imagery applications for precision agriculture - a systemic survey. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:3005-3038. [DOI: 10.1007/s11042-021-11729-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/24/2021] [Accepted: 11/02/2021] [Indexed: 08/02/2023]
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23
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Park E, Kim YS, Omari MK, Suh HK, Faqeerzada MA, Kim MS, Baek I, Cho BK. High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng ( Panax ginseng Meyer) Using a Hyperspectral Reflectance Image. SENSORS 2021; 21:s21165634. [PMID: 34451076 PMCID: PMC8402434 DOI: 10.3390/s21165634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/15/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022]
Abstract
Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
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Affiliation(s)
- Eunsoo Park
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Yun-Soo Kim
- R&D Headquarters, Korea Ginseng Corporation, Daejeon 34128, Korea;
| | - Mohammad Kamran Omari
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Hyun-Kwon Suh
- Department of Life Resources Industry, Dong-A University, Busan 49315, Korea;
| | - Mohammad Akbar Faqeerzada
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville MD 20705, USA; (M.S.K.); (I.B.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon 34134, Korea; (E.P.); (M.K.O.); (M.A.F.)
- Department of Smart Agriculture System, Chungnam National University, Daejeon 34134, Korea
- Correspondence:
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24
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Wu N, Liu F, Meng F, Li M, Zhang C, He Y. Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning. Front Bioeng Biotechnol 2021; 9:696292. [PMID: 34368096 PMCID: PMC8343196 DOI: 10.3389/fbioe.2021.696292] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/23/2021] [Indexed: 11/13/2022] Open
Abstract
Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.
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Affiliation(s)
- Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fanjia Meng
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Mu Li
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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25
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Grzybowski M, Wijewardane NK, Atefi A, Ge Y, Schnable JC. Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges. PLANT COMMUNICATIONS 2021; 2:100209. [PMID: 34327323 PMCID: PMC8299078 DOI: 10.1016/j.xplc.2021.100209] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/23/2021] [Accepted: 05/24/2021] [Indexed: 05/05/2023]
Abstract
Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively. This has limited the use of information on natural variation in nutrient and metabolite abundance, as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants. A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data, primarily in ecophysiological contexts. Here, we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping, and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts. The performances of previously published models in estimating six traits from hyperspectral reflectance data in maize were evaluated on new sample datasets, and the resulting predicted trait values shown to be heritable (e.g., explained by genetic factors) were estimated. The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.
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Affiliation(s)
- Marcin Grzybowski
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Nuwan K. Wijewardane
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Agricultural Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - James C. Schnable
- Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Corresponding author
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26
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Abstract
Leaf nitrogen (N) directly correlates to chlorophyll production, affecting crop growth and yield. Farmers use soil plant analysis development (SPAD) devices to calculate the amount of chlorophyll present in plants. However, monitoring large-scale crops using SPAD is prohibitively time-consuming and demanding. This paper presents an unmanned aerial vehicle (UAV) solution for estimating leaf N content in rice crops, from multispectral imagery. Our contribution is twofold: (i) a novel trajectory control strategy to reduce the angular wind-induced perturbations that affect image sampling accuracy during UAV flight, and (ii) machine learning models to estimate the canopy N via vegetation indices (VIs) obtained from the aerial imagery. This approach integrates an image processing algorithm using the GrabCut segmentation method with a guided filtering refinement process, to calculate the VIs according to the plots of interest. Three machine learning methods based on multivariable linear regressions (MLR), support vector machines (SVM), and neural networks (NN), were applied and compared through the entire phonological cycle of the crop: vegetative (V), reproductive (R), and ripening (Ri). Correlations were obtained by comparing our methods against an assembled ground-truth of SPAD measurements. The higher N correlations were achieved with NN: 0.98 (V), 0.94 (R), and 0.89 (Ri). We claim that the proposed UAV stabilization control algorithm significantly improves on the N-to-SPAD correlations by minimizing wind perturbations in real-time and reducing the need for offline image corrections.
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27
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Liu S, Zhong H, Meng X, Sun T, Li Y, Pinson SRM, Chang SKC, Peng Z. Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods. BMC PLANT BIOLOGY 2020; 20:441. [PMID: 32972357 PMCID: PMC7513512 DOI: 10.1186/s12870-020-02603-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 08/16/2020] [Indexed: 05/08/2023]
Abstract
BACKGROUND Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers. RESULTS To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10- 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods. CONCLUSIONS While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.
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Affiliation(s)
- Shuai Liu
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Hua Zhong
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, China
| | - Xiaoxi Meng
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA
| | - Tong Sun
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, China
| | - Yangsheng Li
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan, China
| | - Shannon R M Pinson
- Dale Bumpers National Rice Research Center, USDA ARS, Stuttgart, AR, 72160, USA
| | - Sam K C Chang
- Experimental Seafood Processing Laboratory, Coastal and Research Extension Center, Mississippi State University, Pascagoula, MS, 39567, USA
| | - Zhaohua Peng
- Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Starkville, MS, 39762, USA.
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Leisner CP. Review: Climate change impacts on food security- focus on perennial cropping systems and nutritional value. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 293:110412. [PMID: 32081261 DOI: 10.1016/j.plantsci.2020.110412] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 12/09/2019] [Accepted: 01/08/2020] [Indexed: 05/18/2023]
Abstract
Anthropogenic increases in fossil fuel emissions have been a primary driver of increased concentrations of atmospheric carbon dioxide ([CO2]) and other greenhouse gases resulting in warmer temperatures, alterations in precipitation patterns, and increased occurrence of extreme weather events in terrestrial areas across the globe. In agricultural growing regions, alterations in climate can challenge plant productivity in ways that impact the ability of the world to sustain adequate food production for a growing and increasingly affluent population with shifting access to affordable and nutritious food. While the knowledge gap that exists regarding potential climate change impacts is large across agriculture, it is especially large in specialty cropping systems. This includes fruit and vegetable crops, and perennial cropping systems which also contribute (along with row crops) to our global diet. In order to obtain a comprehensive view of the true impact of climate change on our global food supply, we must expand our narrow focus from improving yield and plant productivity to include the impact of climate change on the nutritional value of these crops. In order to address these questions, we need a multi-faceted approach that integrates physiology and genomics tools and conducts comprehensive experiments under realistic depictions of future projected climate. This review describes gaps in our knowledge in relation to these responses, and future questions and actions that are needed to develop a sustainable future food supply in light of global climate change.
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Affiliation(s)
- Courtney P Leisner
- Department of Biological Sciences, Auburn University, Auburn AL 36849 USA.
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Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, Xiong L, Yan J. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. MOLECULAR PLANT 2020; 13:187-214. [PMID: 31981735 DOI: 10.1016/j.molp.2020.01.008] [Citation(s) in RCA: 239] [Impact Index Per Article: 59.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/06/2020] [Accepted: 01/10/2020] [Indexed: 05/18/2023]
Abstract
Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.
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Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China.
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crops Science/College of Agronomy, Henan Agricultural University, Zhengzhou 450002, P.R. China
| | - Jian Zhang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - John H Doonan
- The National Plant Phenomics Centre, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | | | - Lizhong Xiong
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, P.R. China
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Anandan A, Mahender A, Sah RP, Bose LK, Subudhi H, Meher J, Reddy JN, Ali J. Non-destructive phenotyping for early seedling vigor in direct-seeded rice. PLANT METHODS 2020; 16:127. [PMID: 32973913 PMCID: PMC7507283 DOI: 10.1186/s13007-020-00666-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 09/04/2020] [Indexed: 05/17/2023]
Abstract
BACKGROUND Early seedling vigor is an essential trait of direct-seeded rice. It helps the seedlings to compete with weeds for water and nutrient availability, and contributes to better seedling establishment during the initial phase of crop growth. Seedling vigor is a complex trait, and phenotyping by a destructive method limits the improvement of this trait through traditional breeding. Hence, a non-invasive, rapid, and precise image-based phenotyping technique is developed to increase the possibility to improve early seedling vigor through breeding in rice and other field crops. RESULTS To establish and assess the methodology using free-source software, early seedling vigor was estimated from images captured with a digital SLR camera in a non-destructive way. Here, the legitimacy and strength of the method have been proved through screening seven diverse rice cultivars varying for early seedling vigor. In the regression analysis, whole-plant area (WPA) estimated by destructive-flatbed scanner (WPAs) and non-destructive imaging (WPAi) approaches was strongly related (R2 > 83%) and suggested that WPAi can be adapted in place of destructive methods to estimate seedling vigor. In addition, this study has identified a set of new geometric traits (convex hull and top view area) for screening breeding lines for early seedling vigor in rice, which decreased the time by 80% and halved the cost of labor in data observation. CONCLUSIONS The method demonstrated here is affordable and easy to establish as a phenotypic platform. It is suitable for most glasshouses/net houses for characterizing genotypes to understand the plasticity of shoots under a given environment at the seedling stage. The methodology explained in this experiment has been proven to be practical and suggested as a technique for researchers involved in direct-seeded rice. Consequently, it will help in the simultaneous screening of genotypes in large numbers, the identification of donors, and in gaining information on the genetic basis of the trait to design a breeding program for direct-seeded rice.
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Affiliation(s)
- Annamalai Anandan
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Anumalla Mahender
- Rice Breeding Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, 4031 Philippines
| | - Rameswar Prasad Sah
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Lotan Kumar Bose
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Hatanath Subudhi
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Jitendra Meher
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Janga Nagi Reddy
- Crop Improvement Division, Indian Council of Agricultural Research-National Rice Research Institute (ICAR-NRRI), Cuttack, Odisha 753006 India
| | - Jauhar Ali
- Rice Breeding Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, 4031 Philippines
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