1
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Ssali Nantongo J, Serunkuma E, Burgos G, Nakitto M, Davrieux F, Ssali R. Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124406. [PMID: 38759574 DOI: 10.1016/j.saa.2024.124406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/19/2024]
Abstract
It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 - 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 - 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance.
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Affiliation(s)
| | - Edwin Serunkuma
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda
| | | | - Mariam Nakitto
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda
| | | | - Reuben Ssali
- International Potato Center, Ntinda II Road, Plot 47, P.O Box 22274 Kampala, Uganda.
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2
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Zhang C, Kong J, Wang Z, Tu C, Li Y, Wu D, Song H, Zhao W, Feng S, Guan Z, Ding B, Chen F. Origami-inspired highly stretchable and breathable 3D wearable sensors for in-situ and online monitoring of plant growth and microclimate. Biosens Bioelectron 2024; 259:116379. [PMID: 38749288 DOI: 10.1016/j.bios.2024.116379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/03/2024] [Accepted: 05/10/2024] [Indexed: 06/03/2024]
Abstract
The emerging wearable plant sensors demonstrate the capability of in-situ measurement of physiological and micro-environmental information of plants. However, the stretchability and breathability of current wearable plant sensors are restricted mainly due to their 2D planar structures, which interfere with plant growth and development. Here, origami-inspired 3D wearable sensors have been developed for plant growth and microclimate monitoring. Unlike 2D counterparts, the 3D sensors demonstrate theoretically infinitely high stretchability and breathability derived from the structure rather than the material. They are adjusted to 100% and 111.55 mg cm-2·h-1 in the optimized design. In addition to stretchability and breathability, the structural parameters are also used to control the strain distribution of the 3D sensors to enhance sensitivity and minimize interference. After integrating with corresponding sensing materials, electrodes, data acquisition and transmission circuits, and a mobile App, a miniaturized sensing system is produced with the capability of in-situ and online monitoring of plant elongation and microclimate. As a demonstration, the 3D sensors are worn on pumpkin leaves, which can accurately monitor the leaf elongation and microclimate with negligible hindrance to plant growth. Finally, the effects of the microclimate on the plant growth is resolved by analyzing the monitored data. This study would significantly promote the development of wearable plant sensors and their applications in the fields of plant phenomics, plant-environment interface, and smart agriculture.
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Affiliation(s)
- Cheng Zhang
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China; State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China; Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China.
| | - Jingjing Kong
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Ziru Wang
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Chengjin Tu
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yecheng Li
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Daosheng Wu
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Hongbo Song
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenfei Zhao
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shichao Feng
- College of Engineering, Nanjing Agricultural University, Nanjing, 210095, China
| | - Zhiyong Guan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China; Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China
| | - Baoqing Ding
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China; Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing, 210095, China; Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing, 210014, China
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3
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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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4
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Ding JT, Peng YY, Huang M, Zhou SJ. AgriGAN: unpaired image dehazing via a cycle-consistent generative adversarial network for the agricultural plant phenotype. Sci Rep 2024; 14:14994. [PMID: 38951207 PMCID: PMC11217274 DOI: 10.1038/s41598-024-65540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 06/20/2024] [Indexed: 07/03/2024] Open
Abstract
Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agricultural image dehazing method used for extracting such information is limited due to unclear texture details and color representation in the images. To address these limitations, we propose AgriGAN (unpaired image dehazing via a cycle-consistent generative adversarial network) for enhancing the dehazing performance in agricultural plant phenotyping. The algorithm incorporates an atmospheric scattering model to improve the discriminator model and employs a whole-detail consistent discrimination approach to enhance discriminator efficiency, thereby accelerating convergence towards Nash equilibrium state within the adversarial network. Finally, by training with network adversarial loss + cycle consistent loss, clear images are obtained after dehazing process. Experimental evaluations and comparative analysis were conducted to assess this algorithm's performance, demonstrating improved accuracy in dehazing agricultural images while preserving detailed texture information and mitigating color deviation issues.
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Affiliation(s)
- Jin-Ting Ding
- School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Yong-Yu Peng
- School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Min Huang
- School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015, Zhejiang, China
| | - Sheng-Jun Zhou
- Zhejiang Academy of Agricultural Sciences, Hangzhou, 310021, Zhejiang, China.
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5
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Wang X, Xu Y, Wei X. Phenotypic characteristics of the mycelium of Pleurotus geesteranus using image recognition technology. Front Bioeng Biotechnol 2024; 12:1338276. [PMID: 38952667 PMCID: PMC11215179 DOI: 10.3389/fbioe.2024.1338276] [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: 11/14/2023] [Accepted: 05/14/2024] [Indexed: 07/03/2024] Open
Abstract
Phenotypic analysis has significant potential for aiding breeding efforts. However, there is a notable lack of studies utilizing phenotypic analysis in the field of edible fungi. Pleurotus geesteranus is a lucrative edible fungus with significant market demand and substantial industrial output, and early-stage phenotypic analysis of Pleurotus geesteranus is imperative during its breeding process. This study utilizes image recognition technology to investigate the phenotypic features of the mycelium of P. geesteranus. We aim to establish the relations between these phenotypic characteristics and mycelial quality. Four groups of mycelia, namely, the non-degraded and degraded mycelium and the 5th and 14th subcultures, are used as image sources. Two categories of phenotypic metrics, outline and texture, are quantitatively calculated and analyzed. In the outline features of the mycelium, five indexes, namely, mycelial perimeter, radius, area, growth rate, and change speed, are proposed to demonstrate mycelial growth. In the texture features of the mycelium, five indexes, namely, mycelial coverage, roundness, groove depth, density, and density change, are studied to analyze the phenotypic characteristics of the mycelium. Moreover, we also compared the cellulase and laccase activities of the mycelium and found that cellulase level was consistent with the phenotypic indices of the mycelium, which further verified the accuracy of digital image processing technology in analyzing the phenotypic characteristics of the mycelium. The results indicate that there are significant differences in these 10 phenotypic characteristic indices ( P < 0.001 ), elucidating a close relationship between phenotypic characteristics and mycelial quality. This conclusion facilitates rapid and accurate strain selection in the early breeding stage of P. geesteranus.
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Affiliation(s)
- Xingyi Wang
- College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Ya Xu
- College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Xuan Wei
- College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou, China
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6
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Yu R, Cao X, Liu J, Nie R, Zhang C, Yuan M, Huang Y, Liu X, Zheng W, Wang C, Wu T, Su B, Kang Z, Zeng Q, Han D, Wu J. Using UAV-Based Temporal Spectral Indices to Dissect Changes in the Stay-Green Trait in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0171. [PMID: 38694449 PMCID: PMC11062509 DOI: 10.34133/plantphenomics.0171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/17/2024] [Indexed: 05/04/2024]
Abstract
Stay-green (SG) in wheat is a beneficial trait that increases yield and stress tolerance. However, conventional phenotyping techniques limited the understanding of its genetic basis. Spectral indices (SIs) as non-destructive tools to evaluate crop temporal senescence provide an alternative strategy. Here, we applied SIs to monitor the senescence dynamics of 565 diverse wheat accessions from anthesis to maturation stages over 2 field seasons. Four SIs (normalized difference vegetation index, green normalized difference vegetation index, normalized difference red edge index, and optimized soil-adjusted vegetation index) were normalized to develop relative stay-green scores (RSGS) as the SG indicators. An RSGS-based genome-wide association study identified 47 high-confidence quantitative trait loci (QTL) harboring 3,079 single-nucleotide polymorphisms associated with SG and 1,085 corresponding candidate genes. Among them, 15 QTL overlapped or were adjacent to known SG-related QTL/genes, while the remaining QTL were novel. Notably, a set of favorable haplotypes of SG-related candidate genes such as TraesCS2A03G1081100, TracesCS6B03G0356400, and TracesCS2B03G1299500 are increasing following the Green Revolution, further validating the feasibility of the pipeline. This study provided a valuable reference for further quantitative SG and genetic research in diverse wheat panels.
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Affiliation(s)
- Rui Yu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaofeng Cao
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jia Liu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Ruiqi Nie
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chuanliang Zhang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Meng Yuan
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yanchuan Huang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xinzhe Liu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Weijun Zheng
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Changfa Wang
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Tingting Wu
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Baofeng Su
- College of Mechanical and Electronic Engineering,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Qingdong Zeng
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
- College of Plant Protection,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dejun Han
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Jianhui Wu
- College of Agronomy,
Northwest A&F University, Yangling, Shaanxi 712100, China
- State Key Laboratory of Crop Stress Resistance and High-Efficiency Production,
Northwest A&F University, Yangling, Shaanxi 712100, China
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7
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Zhou W, Yan Z, Zhang L. A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction. Sci Rep 2024; 14:5905. [PMID: 38467662 PMCID: PMC10928191 DOI: 10.1038/s41598-024-55243-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024] Open
Abstract
To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.
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Affiliation(s)
- Wei Zhou
- Florida Agricultural and Mechanical University, Tallahassee, FL, 32307, USA.
| | - Zhengxiao Yan
- Florida State University, Tallahassee, FL, 32306, USA
| | - Liting Zhang
- Florida State University, Tallahassee, FL, 32306, USA
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8
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Ginzburg DN, Cox JA, Rhee SY. Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress. PLANT DIRECT 2024; 8:e571. [PMID: 38464685 PMCID: PMC10918709 DOI: 10.1002/pld3.571] [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: 12/05/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.
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Affiliation(s)
- Daniel N. Ginzburg
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | - Jack A. Cox
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
| | - Seung Y. Rhee
- Department of Plant BiologyCarnegie Institution for ScienceStanfordCaliforniaUSA
- Present address:
Plant Resilience Institute, Departments of Biochemistry and Molecular Biology, Plant Biology, and Plant, Soil, and Microbial SciencesMichigan State UniversityEast LansingMichiganUSA
- Present address:
Water and Life Interface InstituteEast LansingMichigan48824USA
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9
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Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. HORTICULTURE RESEARCH 2024; 11:uhad289. [PMID: 38487295 PMCID: PMC10939388 DOI: 10.1093/hr/uhad289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/17/2023] [Indexed: 03/17/2024]
Abstract
Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.
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Affiliation(s)
- Steven J Knapp
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Glenn S Cole
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Dominique D A Pincot
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Christine Jade Dilla-Ermita
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Marta Bjornson
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Randi A Famula
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Thomas R Gordon
- Department of Plant Pathology, University of California, One Shields Avenue, Davis, CA 95616, USA
| | - Julia M Harshman
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
| | - Peter M Henry
- Crop Improvement and Protection Research, USDA-ARS, 1636 E. Alisal Street, CA 93905, USA
| | - Mitchell J Feldmann
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
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10
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Ikram M, Rauf A, Rao MJ, Maqsood MFK, Bakhsh MZM, Ullah M, Batool M, Mehran M, Tahira M. CRISPR-Cas9 based molecular breeding in crop plants: a review. Mol Biol Rep 2024; 51:227. [PMID: 38281301 DOI: 10.1007/s11033-023-09086-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/30/2023] [Indexed: 01/30/2024]
Abstract
Traditional crop breeding techniques are not quickly boosting yields to fulfill the expanding population needs. Long crop lifespans hinder the ability of plant breeding to develop superior crop varieties. Due to the arduous crossing, selecting, and challenging processes, it can take decades to establish new varieties with desired agronomic traits. Develop new plant varieties instantly to reduce hunger and improve food security. As a result of the adoption of conventional agricultural techniques, crop genetic diversity has decreased over time. Several traditional and molecular techniques, such as genetic selection, mutant breeding, somaclonal variation, genome-wide association studies, and others, have improved agronomic traits associated with agricultural plant productivity, quality, and resistance to biotic and abiotic stresses. In addition, modern genome editing approaches based on programmable nucleases, CRISPR, and Cas9 proteins have escorted an exciting new era of plant breeding. Plant breeders and scientists worldwide rely on cutting-edge techniques like quick breeding, genome editing tools, and high-throughput phenotyping to boost crop breeding output. This review compiles discoveries in numerous areas of crop breeding, such as using genome editing tools to accelerate the breeding process and create yearly crop generations with the desired features, to describe the shift from conventional to modern plant breeding techniques.
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Affiliation(s)
- Muhammad Ikram
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Abdul Rauf
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
| | - Muhammad Junaid Rao
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, 100 Daxue Rd., Nanning, 530004, China.
| | | | | | - Maaz Ullah
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Maria Batool
- MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Muhammad Mehran
- Key Laboratory of Arable Land Conservation, Huazhong Agricultural University, Ministry of Agriculture, Wuhan, 430070, China
| | - Maryam Tahira
- National Key Laboratory of Horticultural Plant Biology, Ministry of Education, Wuhan, 430070, Hubei, China
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11
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Jing J, Garbeva P, Raaijmakers JM, Medema MH. Strategies for tailoring functional microbial synthetic communities. THE ISME JOURNAL 2024; 18:wrae049. [PMID: 38537571 PMCID: PMC11008692 DOI: 10.1093/ismejo/wrae049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/26/2024] [Indexed: 04/12/2024]
Abstract
Natural ecosystems harbor a huge reservoir of taxonomically diverse microbes that are important for plant growth and health. The vast diversity of soil microorganisms and their complex interactions make it challenging to pinpoint the main players important for the life support functions microbes can provide to plants, including enhanced tolerance to (a)biotic stress factors. Designing simplified microbial synthetic communities (SynComs) helps reduce this complexity to unravel the molecular and chemical basis and interplay of specific microbiome functions. While SynComs have been successfully employed to dissect microbial interactions or reproduce microbiome-associated phenotypes, the assembly and reconstitution of these communities have often been based on generic abundance patterns or taxonomic identities and co-occurrences but have only rarely been informed by functional traits. Here, we review recent studies on designing functional SynComs to reveal common principles and discuss multidimensional approaches for community design. We propose a strategy for tailoring the design of functional SynComs based on integration of high-throughput experimental assays with microbial strains and computational genomic analyses of their functional capabilities.
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Affiliation(s)
- Jiayi Jing
- Bioinformatics Group, Department of Plant Science, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Paolina Garbeva
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Jos M Raaijmakers
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands
| | - Marnix H Medema
- Bioinformatics Group, Department of Plant Science, Wageningen University & Research, Droevendaalsesteeg 1, 6708PB Wageningen, The Netherlands
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Neupane S, Alexander L, Baysal-Gurel F. Evaluation of Hydrangea Cultivars for Tolerance Against Root Rot Caused by Fusarium oxysporum. PLANT DISEASE 2023; 107:3967-3974. [PMID: 37392028 DOI: 10.1094/pdis-11-22-2712-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
Root rot caused by Fusarium oxysporum Schltdl. is a newly identified disease in oakleaf hydrangea. Some cultivars such as Pee Wee and Queen of Hearts grown in pot-in-pot container systems showed root rot symptoms after late spring frost in May 2018 with 40 and 60% incidence in the infected nursery, respectively. This experiment was carried out to evaluate the tolerance among different hydrangea cultivars against root rot caused by F. oxysporum. Fifteen hydrangea cultivars from four different species were selected, and rooted cuttings were prepared from new spring flushes. Twelve plants from each cultivar were transplanted in a 1-gallon pot. Half of transplanted plants (six single plants) were inoculated by drenching 150 ml of F. oxysporum conidial suspension to maintain the concentration of 1 × 106 conidia/ml. Half of the plants remain noninoculated (control) and were drenched with sterile water. After 4 months, root rot was assessed using a scale of 0 to 100% root area affected, and recovery of F. oxysporum was recorded by plating 1-cm root sections in Fusarium selective medium. Fusaric acid (FA) and mannitol were extracted from the roots of inoculated and noninoculated plants to see the effect and role on pathogenesis. Further, mannitol concentration was analyzed using absorption wavelength in a spectrophotometer, and FA was analyzed using high-performance liquid chromatography (HPLC). Results indicated that no cultivars were resistant to F. oxysporum. Cultivars from Hydrangea arborescens, H. macrophylla, and H. paniculata were more tolerant to F. oxysporum compared to cultivars from H. quercifolia. Among H. quercifolia, cultivars Snowflake, John Wayne, and Alice were more tolerant to F. oxysporum.
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Affiliation(s)
- Sandhya Neupane
- Department of Agriculture and Environmental Sciences, Otis L. Floyd Nursery Research Center, College of Agriculture, Tennessee State University, McMinnville, TN
| | - Lisa Alexander
- Otis L. Floyd Nursery Research Center, USDA-ARS, U.S. National Arboretum, McMinnville, TN
| | - Fulya Baysal-Gurel
- Department of Agriculture and Environmental Sciences, Otis L. Floyd Nursery Research Center, College of Agriculture, Tennessee State University, McMinnville, TN
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13
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Wen T, Li JH, Wang Q, Gao YY, Hao GF, Song BA. Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165626. [PMID: 37481085 DOI: 10.1016/j.scitotenv.2023.165626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.
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Affiliation(s)
- Ting Wen
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Jian-Hong Li
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
| | - Qi Wang
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, PR China.
| | - Yang-Yang Gao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China; Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China.
| | - Bao-An Song
- National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, PR China
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14
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Blasch G, Anberbir T, Negash T, Tilahun L, Belayineh FY, Alemayehu Y, Mamo G, Hodson DP, Rodrigues FA. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia. Sci Rep 2023; 13:16768. [PMID: 37798287 PMCID: PMC10556098 DOI: 10.1038/s41598-023-43770-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 09/28/2023] [Indexed: 10/07/2023] Open
Abstract
Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties' response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS-NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.
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Affiliation(s)
- Gerald Blasch
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia.
| | - Tadesse Anberbir
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - Tamirat Negash
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | - Lidiya Tilahun
- Kulumsa Agricultural Research Center (KARC), Asella, Ethiopia
| | | | - Yoseph Alemayehu
- International Maize and Wheat Improvement Center (CIMMYT), Addis Ababa, Ethiopia
| | - Girma Mamo
- Ethiopian Institute of Agricultural Research (EIAR), Addis Ababa, Ethiopia
| | - David P Hodson
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francelino A Rodrigues
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Lincoln Agritech Ltd, Lincoln University, Lincoln, New Zealand
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15
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Cudjoe DK, Virlet N, Castle M, Riche AB, Mhada M, Waine TW, Mohareb F, Hawkesford MJ. Field phenotyping for African crops: overview and perspectives. FRONTIERS IN PLANT SCIENCE 2023; 14:1219673. [PMID: 37860243 PMCID: PMC10582954 DOI: 10.3389/fpls.2023.1219673] [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: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
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Affiliation(s)
- Daniel K. Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Manal Mhada
- AgroBiosciences Department, Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco
| | - Toby W. Waine
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
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16
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Akiyama R, Goto T, Tameshige T, Sugisaka J, Kuroki K, Sun J, Akita J, Hatakeyama M, Kudoh H, Kenta T, Tonouchi A, Shimahara Y, Sese J, Kutsuna N, Shimizu-Inatsugi R, Shimizu KK. Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation. Nat Commun 2023; 14:5792. [PMID: 37737204 PMCID: PMC10517152 DOI: 10.1038/s41467-023-41260-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
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Affiliation(s)
- Reiko Akiyama
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Takao Goto
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Toshiaki Tameshige
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama-Cho, Ikoma, Nara, 630-0192, Japan
| | - Jiro Sugisaka
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, 520-2113, Japan
| | - Ken Kuroki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Jianqiang Sun
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki, 305-8517, Japan
| | - Junichi Akita
- Department of Electric and Computer Engineering, Kanazawa University, Kakuma, Kanazawa, 920-1192, Japan
| | - Masaomi Hatakeyama
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
- Functional Genomics Center Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Hiroshi Kudoh
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, 520-2113, Japan
| | - Tanaka Kenta
- Sugadaira Research Station, Mountain Science Center, University of Tsukuba, 1278-294 Sugadaira-kogen, Ueda, 386-2204, Japan
| | - Aya Tonouchi
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Yuki Shimahara
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Jun Sese
- Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
- Humanome Lab, Inc., L-HUB 3F, 1-4, Shumomiyabi-cho, Shinjuku, Tokyo, 162-0822, Japan
- AIST-Tokyo Tech RWBC-OIL, 2-12-1 O-okayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Natsumaro Kutsuna
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Rie Shimizu-Inatsugi
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
| | - Kentaro K Shimizu
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan.
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Krosney AE, Sotoodeh P, Henry CJ, Beck MA, Bidinosti CP. Inside out: transforming images of lab-grown plants for machine learning applications in agriculture. Front Artif Intell 2023; 6:1200977. [PMID: 37483870 PMCID: PMC10358354 DOI: 10.3389/frai.2023.1200977] [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: 04/05/2023] [Accepted: 06/05/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of different growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. Methods In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. Results Furthermore, we use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. Discussion The inclusion of training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.
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Affiliation(s)
- Alexander E. Krosney
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
| | - Parsa Sotoodeh
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Christopher J. Henry
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Michael A. Beck
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
| | - Christopher P. Bidinosti
- Department of Physics, University of Winnipeg, Winnipeg, MB, Canada
- Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada
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18
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Bhat JA, Feng X, Mir ZA, Raina A, Siddique KHM. Recent advances in artificial intelligence, mechanistic models, and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding. PHYSIOLOGIA PLANTARUM 2023; 175:e13969. [PMID: 37401892 DOI: 10.1111/ppl.13969] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/11/2023] [Accepted: 06/27/2023] [Indexed: 07/05/2023]
Abstract
Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labour intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the nonlinear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to nonlinear modelling approaches in crop breeding, enabling the capture of nonlinear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change.
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Affiliation(s)
| | - Xianzhong Feng
- Zhejiang Lab, Hangzhou, China
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Zahoor A Mir
- ICAR-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Aamir Raina
- Department of Botany, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture and School of Agriculture & Environment, The University of Western Australia, Perth, Western Australia, Australia
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19
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Parveen R, Kumar M, Singh D, Shahani M, Imam Z, Sahoo JP. Understanding the genomic selection for crop improvement: current progress and future prospects. Mol Genet Genomics 2023; 298:813-821. [PMID: 37162565 DOI: 10.1007/s00438-023-02026-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 04/27/2023] [Indexed: 05/11/2023]
Abstract
Although increased use of modern breeding techniques and technology has resulted in long-term genetic gain, the pace of genetic gain must be sped up to satisfy global agricultural demand. However, marker-assisted selection has proven its potential for improving qualitative traits with large effects regulated by one to few genes. Its contribution to the improvement of the quantitative traits regulated by a number of small-effect genes is modest. In this context, genomic selection (GS) has been regarded as the most promising method for genetically enhancing complicated features that are regulated by several genes, each of which has minor effects. By examining a population's phenotypes and high-density marker scores, genomic selection can forecast the breeding potential of individual lines. The fact that GS uses all marker data in the prediction model prevents skewed marker effect estimations and maximizes the amount of variation caused by small-effect QTL. It has the ability to speed up the breeding cycle and as a consequence of which superior genotypes are selected rapidly. Developing the best GS models while taking into account non-additive effects, genotype-by-environment interaction, and cost-effectiveness will enable the widespread implementation of GS in plants. These steps will also increase heritability estimation and prediction accuracy. This review focuses on the shift from conventional selection methods to GS, underlying statistical tools and methodologies, the state of GS research in agricultural plants, and prospects for its effective use in the creation of climate-resilient crops.
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Affiliation(s)
- Rabiya Parveen
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Mankesh Kumar
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Digvijay Singh
- Department of Genetics and Plant Breeding, Narayan Institute of Agricultural Sciences, Gopal Narayan Singh University, Sasaram, 821305, India
| | - Monika Shahani
- Department of Genetics and Plant Breeding, Maharana Pratap University of Agriculture and Technology, Udaipur, 313001, India
| | - Zafar Imam
- Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, Bhagalpur, 813210, India
| | - Jyoti Prakash Sahoo
- Department of Agriculture and Allied Sciences, C.V. Raman Global University, Bhubaneswar, 752054, India.
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20
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Heineck GC, Casanova J, Porter LD. Suitable Methods of Inoculation and Quantification of Fusarium Root Rot in Lentil. PLANT DISEASE 2023:PDIS07221658RE. [PMID: 36265151 DOI: 10.1094/pdis-07-22-1658-re] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Lentil (Lens culinaris L. subsp. culinaris) is an important grain legume grown worldwide. As its popularity grows among consumers and more acres are produced, new root rot complexes have become more prevalent. This work sought to develop methods for studying root rot caused by Fusarium avenaceum in lentil using controlled environments. The objectives were to (i) find an effective and seed-safe sterilization technique, (ii) optimize the inoculation technique and lentil growing environment, and (iii) develop visual and automated disease scoring systems. Results showed the use of detergent and a low concentration (0.1%) of NaClO (the active ingredient in bleach) maintained germinability and effectively eliminated bacterial and fungal contamination on seeds. Other treatments, such as ethanol, reduced seed germination or failed to kill pathogenic fungi such as Fusarium spp. Placing inoculum at a moderate rate of 1 × 106 spores both directly on the seed and on top of the media covering the seed improved severity scores and reduced escapes compared with placement on top of the media only. Visual severity scoring systems and diagrammatic scales were developed for scoring the cotyledon region and roots. A computer vision algorithm was designed to improve the efficiency of scoring the cotyledon region and roots for disease severity using a simple RGB camera and lightbox. Visual and computer scores were best correlated when images were visually scored on a monitor, and multiple images were averaged. The scores generated from the computer vision algorithm had better correlations with visual scores for cotyledon rot (r = 0.92 and β1 = 0.96) than root rot (r = 0.62 and β1 = 0.67).
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Affiliation(s)
- G C Heineck
- USDA-ARS Northwest Sustainable Agroecosystems Research Unit, Washington State University, Prosser, WA 99350
| | - Joaquin Casanova
- USDA-ARS Northwest Sustainable Agroecosystems Research Unit, Washington State University, Pullman, WA 99164
| | - Lyndon D Porter
- USDA-ARS Grain Legume Genetics and Physiology Research Unit, Prosser, WA 99350
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21
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Méline V, Caldwell DL, Kim BS, Khangura RS, Baireddy S, Yang C, Sparks EE, Dilkes B, Delp EJ, Iyer-Pascuzzi AS. Image-based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 113:887-903. [PMID: 36628472 DOI: 10.1111/tpj.16101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 11/12/2022] [Accepted: 01/02/2023] [Indexed: 06/17/2023]
Abstract
A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.
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Affiliation(s)
- Valérian Méline
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Denise L Caldwell
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Bong-Suk Kim
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
| | - Rajdeep S Khangura
- Department of Biochemistry and Center for Plant Biology, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Sriram Baireddy
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Changye Yang
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Erin E Sparks
- Department of Plant and Soil Sciences and the Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
| | - Brian Dilkes
- Department of Biochemistry and Center for Plant Biology, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Edward J Delp
- Video and Image Processing Laboratory (VIPER), School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Anjali S Iyer-Pascuzzi
- Department of Botany and Plant Pathology and Center for Plant Biology, Purdue University, 915 W. State Street, West Lafayette, Indiana, USA
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22
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Abstract
Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.
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23
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Yang Z, Qin F. The battle of crops against drought: Genetic dissection and improvement. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2023; 65:496-525. [PMID: 36639908 DOI: 10.1111/jipb.13451] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
With ongoing global climate change, water scarcity-induced drought stress remains a major threat to agricultural productivity. Plants undergo a series of physiological and morphological changes to cope with drought stress, including stomatal closure to reduce transpiration and changes in root architecture to optimize water uptake. Combined phenotypic and multi-omics studies have recently identified a number of drought-related genetic resources in different crop species. The functional dissection of these genes using molecular techniques has enriched our understanding of drought responses in crops and has provided genetic targets for enhancing resistance to drought. Here, we review recent advances in the cloning and functional analysis of drought resistance genes and the development of technologies to mitigate the threat of drought to crop production.
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Affiliation(s)
- Zhirui Yang
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Feng Qin
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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24
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Gou M, Balint-Kurti P, Xu M, Yang Q. Quantitative disease resistance: Multifaceted players in plant defense. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2023; 65:594-610. [PMID: 36448658 DOI: 10.1111/jipb.13419] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
In contrast to large-effect qualitative disease resistance, quantitative disease resistance (QDR) exhibits partial and generally durable resistance and has been extensively utilized in crop breeding. The molecular mechanisms underlying QDR remain largely unknown but considerable progress has been made in this area in recent years. In this review, we summarize the genes that have been associated with plant QDR and their biological functions. Many QDR genes belong to the canonical resistance gene categories with predicted functions in pathogen perception, signal transduction, phytohormone homeostasis, metabolite transport and biosynthesis, and epigenetic regulation. However, other "atypical" QDR genes are predicted to be involved in processes that are not commonly associated with disease resistance, such as vesicle trafficking, molecular chaperones, and others. This diversity of function for QDR genes contrasts with qualitative resistance, which is often based on the actions of nucleotide-binding leucine-rich repeat (NLR) resistance proteins. An understanding of the diversity of QDR mechanisms and of which mechanisms are effective against which classes of pathogens will enable the more effective deployment of QDR to produce more durably resistant, resilient crops.
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Affiliation(s)
- Mingyue Gou
- State Key Laboratory of Wheat and Maize Crop Science, Collaborative Innovation Center of Henan Grain Crops, Center for Crop Genome Engineering, College of Agronomy, Henan Agricultural University, Zhengzhou, 450002, China
- The Shennong Laboratory, Zhengzhou, 450002, China
| | - Peter Balint-Kurti
- Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA
- Plant Science Research Unit, USDA-ARS, Raleigh, NC, 27695, USA
| | - Mingliang Xu
- State Key Laboratory of Plant Physiology and Biochemistry, National Maize Improvement Center, Center for Crop Functional Genomics and Molecular Breeding, College of Agronomy, China Agricultural University, Beijing, 100193, China
| | - Qin Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region of the Ministry of Agriculture, College of Agronomy, Northwest A&F University, Yangling, 712100, China
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25
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Liu X, Li N, Huang Y, Lin X, Ren Z. A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology. FRONTIERS IN PLANT SCIENCE 2023; 13:1084847. [PMID: 36777535 PMCID: PMC9909479 DOI: 10.3389/fpls.2022.1084847] [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: 10/31/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.
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Affiliation(s)
- Xuan Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Na Li
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Yirui Huang
- College of Information Engineering, Hebei GEO University, Shijiazhuang, China
| | - Xiujun Lin
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Zhenhui Ren
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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26
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Jeger MJ. Tolerance of plant virus disease: Its genetic, physiological, and epidemiological significance. Food Energy Secur 2022. [DOI: 10.1002/fes3.440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Michael John Jeger
- Department of Life Sciences, Silwood Park Imperial College London Ascot UK
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27
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Arumugam T, Hatta MAM. Improving Coconut Using Modern Breeding Technologies: Challenges and Opportunities. PLANTS (BASEL, SWITZERLAND) 2022; 11:3414. [PMID: 36559524 PMCID: PMC9784122 DOI: 10.3390/plants11243414] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/19/2022] [Accepted: 10/26/2022] [Indexed: 06/17/2023]
Abstract
Coconut (Cocos nucifera L.) is a perennial palm with a wide range of distribution across tropical islands and coastlines. Multitude use of coconut by nature is important in the socio-economic fabric framework among rural smallholders in producing countries. It is a major source of income for 30 million farmers, while 60 million households rely on the coconut industry directly as farm workers and indirectly through the distribution, marketing, and processing of coconut and coconut-based products. Stagnant production, inadequate planting materials, the effects of climate change, as well as pests and diseases are among the key issues that need to be urgently addressed in the global coconut industry. Biotechnology has revolutionized conventional breeding approaches in creating genetic variation for trait improvement in a shorter period of time. In this review, we highlighted the challenges of current breeding strategies and the potential of biotechnological approaches, such as genomic-assisted breeding, next-generation sequencing (NGS)-based genotyping and genome editing tools in improving the coconut. Also, combining these technologies with high-throughput phenotyping approaches and speed breeding could speed up the rate of genetic gain in coconut breeding to solve problems that have been plaguing the industry for decades.
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Affiliation(s)
| | - Muhammad Asyraf Md Hatta
- Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
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28
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Sun G, Lu H, Zhao Y, Zhou J, Jackson R, Wang Y, Xu L, Wang A, Colmer J, Ober E, Zhao Q, Han B, Zhou J. AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. THE NEW PHYTOLOGIST 2022; 236:1584-1604. [PMID: 35901246 PMCID: PMC9796158 DOI: 10.1111/nph.18314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/31/2022] [Indexed: 05/11/2023]
Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
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Affiliation(s)
- Gang Sun
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Hengyun Lu
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Yan Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Robert Jackson
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Yongchun Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ling‐xiang Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Ahong Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Joshua Colmer
- Earlham InstituteNorwich Research ParkNorwichNR4 7UHUK
| | - Eric Ober
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
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29
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Khan A, Korban SS. Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3961-3985. [PMID: 35441862 DOI: 10.1007/s00122-022-04093-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/29/2022] [Indexed: 06/14/2023]
Abstract
Climate change, large monocultures of disease-susceptible cultivars, overuse of pesticides, and the emergence of new pathogens or pathogenic strains causing economic losses are all major threats to our environment, health, food, and nutritional supply. Temperate tree fruit crops belonging to the Rosaceae family are the most economically important and widely grown fruit crops. These long-lived crops are under attack from many different pathogens, incurring major economic losses. Multiple chemical sprays to control various diseases annually is a common practice, resulting in significant input costs, as well as environmental and health concerns. Breeding for disease resistance has been undertaken primarily in pome fruit crops (apples and pears) for a few fungal and bacterial diseases, and to a lesser extent in some stone fruit crops. These breeding efforts have taken multiple decades due to the biological constraints and complex genetics of these tree fruit crops. Over the past couple of decades, major advances have been made in genetic and physical mapping, genomics, biotechnology, genome sequencing, and phenomics, along with accumulation of large germplasm collections in repositories. These valuable resources offer opportunities to make significant advances in greatly reducing the time needed to either develop new cultivars or modify existing economic cultivars for enhanced resistance to multiple diseases. This review will cover current knowledge, challenges, and opportunities in breeding for disease resistance in temperate tree fruit crops.
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Affiliation(s)
- Awais Khan
- Plant Pathology and Plant-Microbe Biology Section, Cornell University, Geneva, NY, 14456, USA.
| | - Schuyler S Korban
- Department of Natural Sciences and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
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30
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Guo C, Liu L, Sun H, Wang N, Zhang K, Zhang Y, Zhu J, Li A, Bai Z, Liu X, Dong H, Li C. Predicting F v /F m and evaluating cotton drought tolerance using hyperspectral and 1D-CNN. FRONTIERS IN PLANT SCIENCE 2022; 13:1007150. [PMID: 36330250 PMCID: PMC9623111 DOI: 10.3389/fpls.2022.1007150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
The chlorophyll fluorescence parameter Fv/Fm is significant in abiotic plant stress. Current acquisition methods must deal with the dark adaptation of plants, which cannot achieve rapid, real-time, and high-throughput measurements. However, increased inputs on different genotypes based on hyperspectral model recognition verified its capabilities of handling large and variable samples. Fv/Fm is a drought tolerance index reflecting the best drought tolerant cotton genotype. Therefore, Fv/Fm hyperspectral prediction of different cotton varieties, and drought tolerance evaluation, are worth exploring. In this study, 80 cotton varieties were studied. The hyperspectral cotton data were obtained during the flowering, boll setting, and boll opening stages under normal and drought stress conditions. Next, One-dimensional convolutional neural networks (1D-CNN), Categorical Boosting (CatBoost), Light Gradient Boosting Machines (LightBGM), eXtreme Gradient Boosting (XGBoost), Decision Trees (DT), Random Forests (RF), Gradient elevation decision trees (GBDT), Adaptive Boosting (AdaBoost), Extra Trees (ET), and K-Nearest Neighbors (KNN) were modeled with F v /F m. The Savitzky-Golay + 1D-CNN model had the best robustness and accuracy (RMSE = 0.016, MAE = 0.009, MAPE = 0.011). In addition, the F v /F m prediction drought tolerance coefficient and the manually measured drought tolerance coefficient were similar. Therefore, cotton varieties with different drought tolerance degrees can be monitored using hyperspectral full band technology to establish a 1D-CNN model. This technique is non-destructive, fast and accurate in assessing the drought status of cotton, which promotes smart-scale agriculture.
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Affiliation(s)
- Congcong Guo
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Hongchun Sun
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
- Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China
| | - Ke Zhang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yongjiang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jijie Zhu
- Cotton Research Center, Shandong Key Lab for Cotton Culture and Physiology, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Anchang Li
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Zhiying Bai
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xiaoqing Liu
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Hezhong Dong
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, Hebei, China
| | - Cundong Li
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
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31
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Zandberg JD, Fernandez CT, Danilevicz MF, Thomas WJW, Edwards D, Batley J. The Global Assessment of Oilseed Brassica Crop Species Yield, Yield Stability and the Underlying Genetics. PLANTS (BASEL, SWITZERLAND) 2022; 11:2740. [PMID: 36297764 PMCID: PMC9610009 DOI: 10.3390/plants11202740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/08/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus (rapeseed, canola) production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.
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Affiliation(s)
- Jaco D. Zandberg
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | | | - Monica F. Danilevicz
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - William J. W. Thomas
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - David Edwards
- Center for Applied Bioinformatics, School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
| | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, WA 6009, Australia
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32
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Reinert S. Quantitative genetics of pleiotropy and its potential for plant sciences. JOURNAL OF PLANT PHYSIOLOGY 2022; 276:153784. [PMID: 35944292 DOI: 10.1016/j.jplph.2022.153784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Stephan Reinert
- Friedrich-Alexander-University Erlangen-Nürnberg, Department of Biology, Division of Biochemistry, Biocomputing Lab, Staudtstraße 5, 91058, Erlangen, Germany.
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33
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Qu J, Morota G, Cheng H. A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data. THE PLANT GENOME 2022; 15:e20228. [PMID: 35904052 DOI: 10.1002/tpg2.20228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/07/2022] [Indexed: 06/15/2023]
Abstract
The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.
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Affiliation(s)
- Jiayi Qu
- Dep. of Animal Science, Univ. of California Davis, Davis, CA, 95616, USA
| | - Gota Morota
- Dep. of Animal and Poultry Sciences, Virginia Polytechnic Institute and State Univ., Blacksburg, VA, 24061, USA
| | - Hao Cheng
- Dep. of Animal Science, Univ. of California Davis, Davis, CA, 95616, USA
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Garrett KA, Bebber DP, Etherton BA, Gold KM, Plex Sulá AI, Selvaraj MG. Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation. ANNUAL REVIEW OF PHYTOPATHOLOGY 2022; 60:357-378. [PMID: 35650670 DOI: 10.1146/annurev-phyto-021021-042636] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.
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Affiliation(s)
- K A Garrett
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - D P Bebber
- Department of Biosciences, University of Exeter, Exeter, United Kingdom
| | - B A Etherton
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - K M Gold
- Plant Pathology and Plant Microbe Biology Section, School of Integrative Plant Sciences, Cornell AgriTech, Cornell University, Geneva, New York, USA
| | - A I Plex Sulá
- Plant Pathology Department, University of Florida, Gainesville, Florida, USA;
- Food Systems Institute, University of Florida, Gainesville, Florida, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, USA
| | - M G Selvaraj
- The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Cali, Colombia
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Zhang C, Zhou L, Xiao Q, Bai X, Wu B, Wu N, Zhao Y, Wang J, Feng L. End-to-End Fusion of Hyperspectral and Chlorophyll Fluorescence Imaging to Identify Rice Stresses. PLANT PHENOMICS 2022; 2022:9851096. [PMID: 36059603 PMCID: PMC9394116 DOI: 10.34133/2022/9851096] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/03/2022] [Indexed: 11/07/2022]
Abstract
Herbicides and heavy metals are hazardous substances of environmental pollution, resulting in plant stress and harming humans and animals. Identification of stress types can help trace stress sources, manage plant growth, and improve stress-resistant breeding. In this research, hyperspectral imaging (HSI) and chlorophyll fluorescence imaging (Chl-FI) were adopted to identify the rice plants under two types of herbicide stresses (butachlor (DCA) and quinclorac (ELK)) and two types of heavy metal stresses (cadmium (Cd) and copper (Cu)). Visible/near-infrared spectra of leaves (L-VIS/NIR) and stems (S-VIS/NIR) extracted from HSI and chlorophyll fluorescence kinetic curves of leaves (L-Chl-FKC) and stems (S-Chl-FKC) extracted from Chl-FI were fused to establish the models to detect the stress of the hazardous substances. Novel end-to-end deep fusion models were proposed for low-level, middle-level, and high-level information fusion to improve identification accuracy. Results showed that the high-level fusion-based convolutional neural network (CNN) models reached the highest detection accuracy (97.7%), outperforming the models using a single data source (<94.7%). Furthermore, the proposed end-to-end deep fusion models required a much simpler training procedure than the conventional two-stage deep learning fusion. This research provided an efficient alternative for plant stress phenotyping, including identifying plant stresses caused by hazardous substances of environmental pollution.
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Affiliation(s)
- Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - 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
| | - Baohua Wu
- 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
| | - Na Wu
- 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
| | - Yiying Zhao
- Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Junmin Wang
- Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Lei Feng
- 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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du Toit F, Coops NC, Ratcliffe B, El-Kassaby YA. Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics. FRONTIERS IN PLANT SCIENCE 2022; 13:893017. [PMID: 35909722 PMCID: PMC9330362 DOI: 10.3389/fpls.2022.893017] [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: 03/09/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALSCAN and ALSACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALSCAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALSACC represented the most statistically accurate model. We report that the accuracy of both the ALSCAN (0.8239) and ALSACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs' overall efficiency and cost savings.
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Affiliation(s)
- Francois du Toit
- Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Nicholas C. Coops
- Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
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Danilevicz MF, Gill M, Anderson R, Batley J, Bennamoun M, Bayer PE, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Front Genet 2022; 13:822173. [PMID: 35664329 PMCID: PMC9159391 DOI: 10.3389/fgene.2022.822173] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
- *Correspondence: David Edwards,
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Miao Y, Wang L, Peng C, Li H, Li X, Zhang M. Banana plant counting and morphological parameters measurement based on terrestrial laser scanning. PLANT METHODS 2022; 18:66. [PMID: 35585596 PMCID: PMC9118865 DOI: 10.1186/s13007-022-00894-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/25/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. RESULTS First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. CONCLUSION The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.
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Affiliation(s)
- Yanlong Miao
- Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, 100083, China
| | - Liuyang Wang
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Cheng Peng
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Han Li
- Key Lab of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - Xiuhua Li
- College of Electrical Engineering, Guangxi University, Nanning, 530004, Guangxi, China
| | - Man Zhang
- Key Lab of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, 100083, China.
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Zhou R, Jiang F, Niu L, Song X, Yu L, Yang Y, Wu Z. Increase Crop Resilience to Heat Stress Using Omic Strategies. FRONTIERS IN PLANT SCIENCE 2022; 13:891861. [PMID: 35656008 PMCID: PMC9152541 DOI: 10.3389/fpls.2022.891861] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 04/04/2022] [Indexed: 06/15/2023]
Abstract
Varieties of various crops with high resilience are urgently needed to feed the increased population in climate change conditions. Human activities and climate change have led to frequent and strong weather fluctuation, which cause various abiotic stresses to crops. The understanding of crops' responses to abiotic stresses in different aspects including genes, RNAs, proteins, metabolites, and phenotypes can facilitate crop breeding. Using multi-omics methods, mainly genomics, transcriptomics, proteomics, metabolomics, and phenomics, to study crops' responses to abiotic stresses will generate a better, deeper, and more comprehensive understanding. More importantly, multi-omics can provide multiple layers of information on biological data to understand plant biology, which will open windows for new opportunities to improve crop resilience and tolerance. However, the opportunities and challenges coexist. Interpretation of the multidimensional data from multi-omics and translation of the data into biological meaningful context remained a challenge. More reasonable experimental designs starting from sowing seed, cultivating the plant, and collecting and extracting samples were necessary for a multi-omics study as the first step. The normalization, transformation, and scaling of single-omics data should consider the integration of multi-omics. This review reports the current study of crops at abiotic stresses in particular heat stress using omics, which will help to accelerate crop improvement to better tolerate and adapt to climate change.
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Affiliation(s)
- Rong Zhou
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
- Department of Food Science, Aarhus University, Aarhus, Denmark
| | - Fangling Jiang
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Lifei Niu
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Xiaoming Song
- College of Life Sciences, North China University of Science and Technology, Tangshan, China
| | - Lu Yu
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
| | - Yuwen Yang
- Excellence and Innovation Center, Jiangsu Academy of Agricultural Sciences, Nanjing, China
| | - Zhen Wu
- College of Horticulture, Nanjing Agricultural University, Nanjing, China
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40
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Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14102292] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Crop height is an essential parameter used to monitor overall crop growth, forecast crop yield, and estimate crop biomass in precision agriculture. However, individual maize segmentation is the prerequisite for precision field monitoring, which is a challenging task because the maize stalks are usually occluded by leaves between adjacent plants, especially when they grow up. In this study, we proposed a novel method that combined seedling detection and clustering algorithms to segment individual maize plants from UAV-borne LiDAR and RGB images. As seedlings emerged, the images collected by an RGB camera mounted on a UAV platform were processed and used to generate a digital orthophoto map. Based on this orthophoto, the location of each maize seedling was identified by extra-green detection and morphological filtering. A seed point set was then generated and used as input for the clustering algorithm. The fuzzy C-means clustering algorithm was used to segment individual maize plants. We computed the difference between the maximum elevation value of the LiDAR point cloud and the average elevation value of the bare digital terrain model (DTM) at each corresponding area for individual plant height estimation. The results revealed that our height estimation approach test on two cultivars produced the accuracy with R2 greater than 0.95, with the mean square error (RMSE) of 4.55 cm, 3.04 cm, and 3.29 cm, as well as the mean absolute percentage error (MAPE) of 3.75%, 0.91%, and 0.98% at three different growth stages, respectively. Our approach, utilizing UAV-borne LiDAR and RGB cameras, demonstrated promising performance for estimating maize height and its field position.
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An Integration of Linear Model and ‘Random Forest’ Techniques for Prediction of Norway Spruce Vitality: A Case Study of the Hemiboreal Forest, Latvia. REMOTE SENSING 2022. [DOI: 10.3390/rs14092122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The increasing extreme weather and climate events have a significant impact on the resistance and resilience of Norway spruce trees. The responses and adaptation of individual trees to certain factors can be assessed through the tree breeding programmes. Tree breeding programmes combined with multispectral unmanned aircraft vehicle (UAV) platforms may assist in acquiring regular information of individual traits from large areas of progeny trials. Therefore, the aim of this study was to investigate the vegetation indices (VI) to detect the early stages of tree stress in Norway spruce stands under prolonged drought and summer heatwave. Eight plots within four stands throughout the vegetation season of 2021 were monitored by assessing spectral differences of tree health classes (Healthy, Crown damage, New crown damage, Dead trees, Stem damage, Root rot). From all tested VI, our models showed a moderate marginal R2 and total explanatory power—for Normalized Difference Red-edge Index (NDRE), marginal R2 was 0.26, and conditional R2 was 0.49 (p < 0.001); for Normalized Difference Vegetation Index (NDVI), marginal R2 was 0.34, and conditional R2 was 0.60 (p < 0.001); for Red Green Index (RGI), marginal R2 was 0.36, and conditional R2 was 0.55 (p < 0.001); while for Chlorophyll Index (CI), marginal R2 was 0.27, and conditional R2 was 0.49 (p < 0.001). The reliability of the identification of tree health classes for selected VI was weak to fair (overall classification accuracy ranged from 34.4% to 56.8%, kappa coefficients ranged from 0.09 to 0.34) if six classes were assessed, and moderate to substantial (overall classification accuracy ranged from 71.1% to 89.6% and kappa coefficient from 0.39 to 0.71) if two classes (Crown damage and Healthy trees) were tested.
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Mira AF, Marques L, Magalhães S, Rodrigues LR. A Method to Measure the Damage Caused by Cell-Sucking Herbivores. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2494:299-312. [PMID: 35467216 DOI: 10.1007/978-1-0716-2297-1_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
The damage that herbivores inflict on plants is a key component of their interaction. Several methods have been proposed to quantify the damage caused by chewing insects, but such methods are not very successful when the damage is inflicted by a cell-sucking organism. Here, we present a protocol that allows a non-destructive quantification of the damage inflicted by cell-sucking arthropods, robustly filtering out leaf vascular structures that might be mistakenly classified as damage in many plant species. The protocol is set for the laboratory environment and uses Fiji and ilastik, two free software packages.
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Affiliation(s)
- André F Mira
- Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Luís Marques
- Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.,Biosystems & Integrative Sciences Institute, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Sara Magalhães
- Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Lisbon, Portugal
| | - Leonor R Rodrigues
- Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Lisbon, Portugal.
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Machine Learning for Image Analysis: Leaf Disease Segmentation. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2443:429-449. [PMID: 35037219 DOI: 10.1007/978-1-0716-2067-0_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, generating massive amounts of data, which require an automated analysis to extract meaningful information. Deep learning is a branch of machine learning that has revolutionized many fields of research. Deep learning models are able to extract autonomously the underlying features within the dataset, providing a multi-level representation of the data. Our intention is to show the feasibility and effectiveness of using deep learning and low-cost technology for automated phenotyping. In this methods chapter, we describe how to train a deep neural network to segment leaf images and extract the pixels related to the disease.
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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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Coulibaly M, Bodjrenou G, Akohoue F, Agoyi EE, Merinosy Francisco FM, Agossou COA, Sawadogo M, Achigan-Dako EG. Profiling Cultivars Development in Kersting's Groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] for Improved Yield, Higher Nutrient Content, and Adaptation to Current and Future Climates. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2022. [DOI: 10.3389/fsufs.2021.759575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Kersting's groundnut [Macrotyloma geocarpum (Harms.) Maréchal and Baudet], Fabaceae, is an important source of protein and essential amino acids. As a grain legume species, it also contributes to improving soil fertility through symbiotic nitrogen fixation. However, the crop is characterized by a relatively low yield (≤500 kg/ha), and limited progress has been made so far, toward the development of high-yielding cultivars that can enhance and sustain its productivity. Recently, there was an increased interest in alleviating the burdens related to Kersting's groundnut (KG) cultivation through the development of improved varieties. Preliminary investigations assembled germplasms from various producing countries. In-depth ethnobotanical studies and insightful investigation on the reproductive biology of the species were undertaken alongside morphological, biochemical, and molecular characterizations. Those studies revealed a narrow genetic base for KG. In addition, the self-pollinating nature of its flowers prevents cross-hybridization and represents a major barrier limiting the broadening of the genetic basis. Therefore, the development of a research pipeline to address the bottlenecks specific to KG is a prerequisite for the successful expansion of the crop. In this paper, we offer an overview of the current state of research on KG and pinpoint the knowledge gaps; we defined and discussed the main steps of breeding for KG' cultivars development; this included (i) developing an integrated genebank, inclusive germplasm, and seed system management; (ii) assessing end-users preferences and possibility for industrial exploitation of the crop; (iii) identifying biotic and abiotic stressors and the genetic control of responsive traits to those factors; (iv) overcoming the cross-pollination challenges in KG to propel the development of hybrids; (v) developing new approaches to create variability and setting adequate cultivars and breeding approaches; (vi) karyotyping and draft genome analysis to accelerate cultivars development and increase genetic gains; and (vii) evaluating the adaptability and stability of cultivars across various ecological regions.
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Varona L, Legarra A, Toro MA, Vitezica ZG. Genomic Prediction Methods Accounting for Nonadditive Genetic Effects. Methods Mol Biol 2022; 2467:219-243. [PMID: 35451778 DOI: 10.1007/978-1-0716-2205-6_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of genomic information for prediction of future phenotypes or breeding values for the candidates to selection has become a standard over the last decade. However, most procedures for genomic prediction only consider the additive (or substitution) effects associated with polymorphic markers. Nevertheless, the implementation of models that consider nonadditive genetic variation may be interesting because they (1) may increase the ability of prediction, (2) can be used to define mate allocation procedures in plant and animal breeding schemes, and (3) can be used to benefit from nonadditive genetic variation in crossbreeding or purebred breeding schemes. This study reviews the available methods for incorporating nonadditive effects into genomic prediction procedures and their potential applications in predicting future phenotypic performance, mate allocation, and crossbred and purebred selection. Finally, a brief outline of some future research lines is also proposed.
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Affiliation(s)
- Luis Varona
- Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Zaragoza, Spain.
- Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain.
| | | | - Miguel A Toro
- Dpto. Producción Agraria, ETS Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
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Wu S, Wen W, Gou W, Lu X, Zhang W, Zheng C, Xiang Z, Chen L, Guo X. A miniaturized phenotyping platform for individual plants using multi-view stereo 3D reconstruction. FRONTIERS IN PLANT SCIENCE 2022; 13:897746. [PMID: 36003825 PMCID: PMC9393617 DOI: 10.3389/fpls.2022.897746] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/08/2022] [Indexed: 05/14/2023]
Abstract
Plant phenotyping is essential in plant breeding and management. High-throughput data acquisition and automatic phenotypes extraction are common concerns in plant phenotyping. Despite the development of phenotyping platforms and the realization of high-throughput three-dimensional (3D) data acquisition in tall plants, such as maize, handling small-size plants with complex structural features remains a challenge. This study developed a miniaturized shoot phenotyping platform MVS-Pheno V2 focusing on low plant shoots. The platform is an improvement of MVS-Pheno V1 and was developed based on multi-view stereo 3D reconstruction. It has the following four components: Hardware, wireless communication and control, data acquisition system, and data processing system. The hardware sets the rotation on top of the platform, separating plants to be static while rotating. A novel local network was established to realize wireless communication and control; thus, preventing cable twining. The data processing system was developed to calibrate point clouds and extract phenotypes, including plant height, leaf area, projected area, shoot volume, and compactness. This study used three cultivars of wheat shoots at four growth stages to test the performance of the platform. The mean absolute percentage error of point cloud calibration was 0.585%. The squared correlation coefficient R 2 was 0.9991, 0.9949, and 0.9693 for plant height, leaf length, and leaf width, respectively. The root mean squared error (RMSE) was 0.6996, 0.4531, and 0.1174 cm for plant height, leaf length, and leaf width. The MVS-Pheno V2 platform provides an alternative solution for high-throughput phenotyping of low individual plants and is especially suitable for shoot architecture-related plant breeding and management studies.
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Affiliation(s)
- Sheng Wu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Wenbo Gou
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Wenqi Zhang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chenxi Zheng
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Zhiwei Xiang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Liping Chen
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- *Correspondence: Liping Chen
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang, China
- Xinyu Guo
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Waiphara P, Bourgenot C, Compton LJ, Prashar A. Optical Imaging Resources for Crop Phenotyping and Stress Detection. Methods Mol Biol 2022; 2494:255-265. [PMID: 35467213 DOI: 10.1007/978-1-0716-2297-1_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
With a rapidly increasing population, diminishing resource availability, and variation in environment, there is a need to change agricultural production to deliver long-term food security. To deliver such change, we need crops that are productive and tolerant to different stress factors. The traditional methods of obtaining data for phenotyping under field conditions, e.g., for morphological traits such as canopy structure or physiological traits such as plant stress-related traits, are laborious and time-consuming. A variety of imaging tools in the visible, spectral, and thermal infrared ranges allow data collection for quantitative studies of complex traits and crop monitoring. These tools can be used on crop phenotyping and monitoring platforms for high-throughput assessment of traits in order to better understand plant stress responses and the physiological pathways underlying yield. The applications and brief review of these imaging techniques are described and discussed in this chapter.
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Affiliation(s)
- Phatchareeya Waiphara
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Cyril Bourgenot
- Precision Optics Laboratory, Durham University, Sedgefield, UK
| | | | - Ankush Prashar
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, UK.
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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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Banerjee BP, Spangenberg G, Kant S. CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements. BIOSENSORS 2021; 12:bios12010016. [PMID: 35049643 PMCID: PMC8774002 DOI: 10.3390/bios12010016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/25/2021] [Accepted: 12/27/2021] [Indexed: 05/12/2023]
Abstract
The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
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Affiliation(s)
| | - German Spangenberg
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia;
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3083, Australia
- Correspondence: ; Tel.: +61-3-4344-3179
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