1
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Mustafa G, Liu Y, Khan IH, Hussain S, Jiang Y, Liu J, Arshad S, Osman R. Establishing a knowledge structure for yield prediction in cereal crops using unmanned aerial vehicles. FRONTIERS IN PLANT SCIENCE 2024; 15:1401246. [PMID: 39184579 PMCID: PMC11341481 DOI: 10.3389/fpls.2024.1401246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/15/2024] [Indexed: 08/27/2024]
Abstract
Recently, a rapid advancement in using unmanned aerial vehicles (UAVs) for yield prediction (YP) has led to many YP research findings. This study aims to visualize the intellectual background, research progress, knowledge structure, and main research frontiers of the entire YP domain for main cereal crops using VOSviewer and a comprehensive literature review. To develop visualization networks of UAVs related knowledge for YP of wheat, maize, rice, and soybean (WMRS) crops, the original research articles published between January 2001 and August 2023 were retrieved from the web of science core collection (WOSCC) database. Significant contributors have been observed to the growth of YP-related research, including the most active countries, prolific publications, productive writers and authors, the top contributing institutions, influential journals, papers, and keywords. Furthermore, the study observed the primary contributions of YP for WMRS crops using UAVs at the micro, meso, and macro levels and the degree of collaboration and information sources for YP. Moreover, the policy assistance from the People's Republic of China, the United States of America, Germany, and Australia considerably advances the knowledge of UAVs connected to YP of WMRS crops, revealed under investigation of grants and collaborating nations. Lastly, the findings of WMRS crops for YP are presented regarding the data type, algorithms, results, and study location. The remote sensing community can significantly benefit from this study by being able to discriminate between the most critical sub-domains of the YP literature for WMRS crops utilizing UAVs and to recommend new research frontiers for concentrating on the essential directions for subsequent studies.
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Affiliation(s)
- Ghulam Mustafa
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, China
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Yuhong Liu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, China
| | - Imran Haider Khan
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Sarfraz Hussain
- College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, China
| | - Yuhan Jiang
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, China
| | - Jiayuan Liu
- Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing, China
| | - Saeed Arshad
- College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Raheel Osman
- Department of Agronomy, Iowa State University, Ames, IA, United States
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2
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Sharma A, Hazarika M, Heisnam P, Pandey H, Devadas VASN, Kesavan AK, Kumar P, Singh D, Vashishth A, Jha R, Misra V, Kumar R. Controlled Environment Ecosystem: A Cutting-Edge Technology in Speed Breeding. ACS OMEGA 2024; 9:29114-29138. [PMID: 39005787 PMCID: PMC11238293 DOI: 10.1021/acsomega.3c09060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/25/2024] [Accepted: 05/31/2024] [Indexed: 07/16/2024]
Abstract
The controlled environment ecosystem is a meticulously designed plant growing chamber utilized for cultivating biofortified crops and microgreens, addressing hidden hunger and malnutrition prevalent in the growing population. The integration of speed breeding within such controlled environments effectively eradicates morphological disruptions encountered in traditional breeding methods such as inbreeding depression, male sterility, self-incompatibility, embryo abortion, and other unsuccessful attempts. In contrast to the unpredictable climate conditions that often prolong breeding cycles to 10-15 years in traditional breeding and 4-5 years in transgenic breeding within open ecosystems, speed breeding techniques expedite the achievement of breeding objectives and F1-F6 generations within 2-3 years under controlled growing conditions. In comparison, traditional breeding may take 5-10 years for plant population line creation, 3-5 years for field trials, and 1-2 years for variety release. The effectiveness of speed breeding in trait improvement and population line development varies across different crops, requiring approximately 4 generations in rice and groundnut, 5 generations in soybean, pea, and oat, 6 generations in sorghum, Amaranthus sp., and subterranean clover, 6-7 generations in bread wheat, durum wheat, and chickpea, 7 generations in broad bean, 8 generations in lentil, and 10 generations in Arabidopsis thaliana annually within controlled environment ecosystems. Artificial intelligence leverages neural networks and algorithm models to screen phenotypic traits and assess their role in diverse crop species. Moreover, in controlled environment systems, mechanistic models combined with machine learning effectively regulate stable nutrient use efficiency, water use efficiency, photosynthetic assimilation product, metabolic use efficiency, climatic factors, greenhouse gas emissions, carbon sequestration, and carbon footprints. However, any negligence, even minor, in maintaining optimal photoperiodism, temperature, humidity, and controlling pests or diseases can lead to the deterioration of crop trials and speed breeding techniques within the controlled environment system. Further comparative studies are imperative to comprehend and justify the efficacy of climate management techniques in controlled environment ecosystems compared to natural environments, with or without soil.
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Affiliation(s)
- Avinash Sharma
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Mainu Hazarika
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Punabati Heisnam
- College of Agriculture, Central Agricultural University, Iroisemba, Manipur 795004, India
| | - Himanshu Pandey
- PG Department of Agriculture, Khalsa College, Amritsar, Punjab 143002, India
| | | | - Ajith Kumar Kesavan
- Faculty of Agricultural Sciences, Arunachal University of Studies, Namsai, Arunachal Pradesh 792103, India
| | - Praveen Kumar
- Agricultural Research Station, Agriculture University, Jodhpur, Rajasthan 342304, India
| | - Devendra Singh
- Faculty of Biotechnology, Shri Ramswaroop Memorial University, Barabanki, Uttar Pradesh 225003, India
| | - Amit Vashishth
- Patanjali Herbal Research Department, Patanjali Research Institute, Haridwar, Uttarakhand 249405, India
| | - Rani Jha
- ISBM University, Gariyaband, Chhattishgarh 493996, India
| | - Varucha Misra
- Division of Crop Improvement, ICAR-Indian Institute of Sugarcane Research, Lucknow, Uttar Pradesh 226002, India
| | - Rajeev Kumar
- Division of Plant Physiology and Biochemistry, ICAR-Indian Institute of Sugarcane Research, Lucknow, Uttar Pradesh 226002, India
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Wang L, Cheng Y, Meftaul IM, Luo F, Kabir MA, Doyle R, Lin Z, Naidu R. Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis. Anal Chem 2024; 96:8109-8123. [PMID: 38490962 DOI: 10.1021/acs.analchem.3c05311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Affiliation(s)
- Liang Wang
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Ying Cheng
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Islam Md Meftaul
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
| | - Fang Luo
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, Fuzhou University, Fuzhou, Fjian 350108, China
| | - Muhammad Ashad Kabir
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, New South Wales 2795, Australia
| | - Richard Doyle
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
- Tasmanian Institute of Agriculture (TIA), University of Tasmania, Launceston, Tasmania 7250, Australia
| | - Zhenyu Lin
- Ministry of Education Key Laboratory for Analytical Science of Food Safety and Biology, Fujian Provincial Key Laboratory of Analysis and Detection for Food Safety, Fuzhou University, Fuzhou, Fjian 350108, China
| | - Ravi Naidu
- Global Centre for Environmental Remediation, College of Engineering, Science and Environment, University of Newcastle, Callaghan, New South Wales 2308, Australia
- The Cooperative Research Centre for High-Performance Soils, Callaghan, New South Wales 2308, Australia
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4
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Carlier A, Dandrifosse S, Dumont B, Mercatoris B. Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing. FRONTIERS IN PLANT SCIENCE 2023; 14:1204791. [PMID: 38053768 PMCID: PMC10694231 DOI: 10.3389/fpls.2023.1204791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/30/2023] [Indexed: 12/07/2023]
Abstract
Estimation of biophysical vegetation variables is of interest for diverse applications, such as monitoring of crop growth and health or yield prediction. However, remote estimation of these variables remains challenging due to the inherent complexity of plant architecture, biology and surrounding environment, and the need for features engineering. Recent advancements in deep learning, particularly convolutional neural networks (CNN), offer promising solutions to address this challenge. Unfortunately, the limited availability of labeled data has hindered the exploration of CNNs for regression tasks, especially in the frame of crop phenotyping. In this study, the effectiveness of various CNN models in predicting wheat dry matter, nitrogen uptake, and nitrogen concentration from RGB and multispectral images taken from tillering to maturity was examined. To overcome the scarcity of labeled data, a training pipeline was devised. This pipeline involves transfer learning, pseudo-labeling of unlabeled data and temporal relationship correction. The results demonstrated that CNN models significantly benefit from the pseudolabeling method, while the machine learning approach employing a PLSr did not show comparable performance. Among the models evaluated, EfficientNetB4 achieved the highest accuracy for predicting above-ground biomass, with an R² value of 0.92. In contrast, Resnet50 demonstrated superior performance in predicting LAI, nitrogen uptake, and nitrogen concentration, with R² values of 0.82, 0.73, and 0.80, respectively. Moreover, the study explored multi-output models to predict the distribution of dry matter and nitrogen uptake between stem, inferior leaves, flag leaf, and ear. The findings indicate that CNNs hold promise as accessible and promising tools for phenotyping quantitative biophysical variables of crops. However, further research is required to harness their full potential.
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Affiliation(s)
- Alexis Carlier
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Sébastien Dandrifosse
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benjamin Dumont
- Plant Sciences, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
| | - Benoit Mercatoris
- Biosystems Dynamics and Exchanges, TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, Gembloux, Belgium
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McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to Comparative Developmental Physiology. Front Physiol 2023; 14:1229500. [PMID: 37645563 PMCID: PMC10461620 DOI: 10.3389/fphys.2023.1229500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.
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Affiliation(s)
| | | | | | - Oliver Tills
- School of Biological and Marine Sciences, University of Plymouth, Plymouth, United Kingdom
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6
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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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7
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Čapek D, Safroshkin M, Morales-Navarrete H, Toulany N, Arutyunov G, Kurzbach A, Bihler J, Hagauer J, Kick S, Jones F, Jordan B, Müller P. EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways. Nat Methods 2023:10.1038/s41592-023-01873-4. [PMID: 37156842 DOI: 10.1038/s41592-023-01873-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 04/05/2023] [Indexed: 05/10/2023]
Abstract
Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.
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Affiliation(s)
- Daniel Čapek
- Systems Biology of Development, University of Konstanz, Konstanz, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | | | - Hernán Morales-Navarrete
- Systems Biology of Development, University of Konstanz, Konstanz, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
- Centre for the Advanced Study of Collective Behaviour, Konstanz, Germany
| | - Nikan Toulany
- Systems Biology of Development, University of Konstanz, Konstanz, Germany
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | | | - Anica Kurzbach
- Systems Biology of Development, University of Konstanz, Konstanz, Germany
| | - Johanna Bihler
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Julia Hagauer
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Sebastian Kick
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Felicity Jones
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany
| | - Ben Jordan
- Systems Biology of Development, University of Konstanz, Konstanz, Germany
| | - Patrick Müller
- Systems Biology of Development, University of Konstanz, Konstanz, Germany.
- Friedrich Miescher Laboratory of the Max Planck Society, Tübingen, Germany.
- Centre for the Advanced Study of Collective Behaviour, Konstanz, Germany.
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8
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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9
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Tsai CF, Huang CH, Wu FH, Lin CH, Lee CH, Yu SS, Chan YK, Jan FJ. Intelligent image analysis recognizes important orchid viral diseases. FRONTIERS IN PLANT SCIENCE 2022; 13:1051348. [PMID: 36531380 PMCID: PMC9755359 DOI: 10.3389/fpls.2022.1051348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
Phalaenopsis orchids are one of the most important exporting commodities for Taiwan. Most orchids are planted and grown in greenhouses. Early detection of orchid diseases is crucially valuable to orchid farmers during orchid cultivation. At present, orchid viral diseases are generally identified with manual observation and the judgment of the grower's experience. The most commonly used assays for virus identification are nucleic acid amplification and serology. However, it is neither time nor cost efficient. Therefore, this study aimed to create a system for automatically identifying the common viral diseases in orchids using the orchid image. Our methods include the following steps: the image preprocessing by color space transformation and gamma correction, detection of leaves by a U-net model, removal of non-leaf fragment areas by connected component labeling, feature acquisition of leaf texture, and disease identification by the two-stage model with the integration of a random forest model and an inception network (deep learning) model. Thereby, the proposed system achieved the excellent accuracy of 0.9707 and 0.9180 for the image segmentation of orchid leaves and disease identification, respectively. Furthermore, this system outperformed the naked-eye identification for the easily misidentified categories [cymbidium mosaic virus (CymMV) and odontoglossum ringspot virus (ORSV)] with the accuracy of 0.842 using two-stage model and 0.667 by naked-eye identification. This system would benefit the orchid disease recognition for Phalaenopsis cultivation.
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Affiliation(s)
- Cheng-Feng Tsai
- Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan
| | - Chih-Hung Huang
- Department of Plant Pathology, National Chung Hsing University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
| | - Fu-Hsing Wu
- Department of Health Services Administration, China Medical University, Taichung, Taiwan
| | - Chuen-Horng Lin
- Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Chia-Hwa Lee
- Department of Plant Pathology, National Chung Hsing University, Taichung, Taiwan
- Ph.D. Program in Microbial Genomics, National Chung Hsing University and Academia Sinica, Taichung, Taipei, Taiwan
| | - Shyr-Shen Yu
- Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan
| | - Yung-Kuan Chan
- Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
| | - Fuh-Jyh Jan
- Department of Plant Pathology, National Chung Hsing University, Taichung, Taiwan
- Advanced Plant Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Ph.D. Program in Microbial Genomics, National Chung Hsing University and Academia Sinica, Taichung, Taipei, Taiwan
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10
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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11
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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12
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Khan MHU, Wang S, Wang J, Ahmar S, Saeed S, Khan SU, Xu X, Chen H, Bhat JA, Feng X. Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. Int J Mol Sci 2022; 23:11156. [PMID: 36232455 PMCID: PMC9570104 DOI: 10.3390/ijms231911156] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
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Affiliation(s)
- Muhammad Hafeez Ullah Khan
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Shoudong Wang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
| | - Jun Wang
- Zhejiang Lab, Hangzhou 310012, China
| | - Sunny Ahmar
- Institute of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia, Jagiellonska 28, 40-032 Katowice, Poland
| | - Sumbul Saeed
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Shahid Ullah Khan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | | | | | | | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
- Zhejiang Lab, Hangzhou 310012, China
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13
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Yan J, Wang X. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 111:1527-1538. [PMID: 35821601 DOI: 10.1111/tpj.15905] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Advances in high-throughput omics technologies are leading plant biology research into the era of big data. Machine learning (ML) performs an important role in plant systems biology because of its excellent performance and wide application in the analysis of big data. However, to achieve ideal performance, supervised ML algorithms require large numbers of labeled samples as training data. In some cases, it is impossible or prohibitively expensive to obtain enough labeled training data; here, the paradigms of unsupervised learning (UL) and semi-supervised learning (SSL) play an indispensable role. In this review, we first introduce the basic concepts of ML techniques, as well as some representative UL and SSL algorithms, including clustering, dimensionality reduction, self-supervised learning (self-SL), positive-unlabeled (PU) learning and transfer learning. We then review recent advances and applications of UL and SSL paradigms in both plant systems biology and plant phenotyping research. Finally, we discuss the limitations and highlight the significance and challenges of UL and SSL strategies in plant systems biology.
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Affiliation(s)
- Jun Yan
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100094, China
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
| | - Xiangfeng Wang
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100094, China
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China
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14
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Rai KK. Integrating speed breeding with artificial intelligence for developing climate-smart crops. Mol Biol Rep 2022; 49:11385-11402. [PMID: 35941420 PMCID: PMC9360691 DOI: 10.1007/s11033-022-07769-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 07/05/2022] [Indexed: 11/25/2022]
Abstract
INTRODUCTION In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. MATERIALS AND METHODS The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. CONCLUSIONS Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.
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Affiliation(s)
- Krishna Kumar Rai
- Centre of Advanced Study in Botany, Department of Botany, Institute of Science, Banaras Hindu University (BHU), 221005, Varanasi, Uttar Pradesh, India.
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15
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Pineda M, Pérez-Bueno ML, Barón M. Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris. FRONTIERS IN PLANT SCIENCE 2022; 13:790268. [PMID: 35812917 PMCID: PMC9265216 DOI: 10.3389/fpls.2022.790268] [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: 10/06/2021] [Accepted: 05/23/2022] [Indexed: 06/15/2023]
Abstract
A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI1-DBI3), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms.
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Affiliation(s)
- Mónica Pineda
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
| | - María Luisa Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
- Department of Plant Physiology, Facultad de Farmacia, University of Granada, Granada, Spain
| | - Matilde Barón
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain
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16
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Nabwire S, Wakholi C, Faqeerzada MA, Arief MAA, Kim MS, Baek I, Cho BK. Estimation of Cold Stress, Plant Age, and Number of Leaves in Watermelon Plants Using Image Analysis. FRONTIERS IN PLANT SCIENCE 2022; 13:847225. [PMID: 35251113 PMCID: PMC8895302 DOI: 10.3389/fpls.2022.847225] [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: 01/01/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Watermelon (Citrullus lanatus) is a widely consumed, nutritious fruit, rich in water and sugars. In most crops, abiotic stresses caused by changes in temperature, moisture, etc., are a significant challenge during production. Due to the temperature sensitivity of watermelon plants, temperatures must be closely monitored and controlled when the crop is cultivated in controlled environments. Studies have found direct responses to these stresses include reductions in leaf size, number of leaves, and plant size. Stress diagnosis based on plant morphological features (e.g., shape, color, and texture) is important for phenomics studies. The purpose of this study is to classify watermelon plants exposed to low-temperature stress conditions from the normal ones using features extracted using image analysis. In addition, an attempt was made to develop a model for estimating the number of leaves and plant age (in weeks) using the extracted features. A model was developed that can classify normal and low-temperature stress watermelon plants with 100% accuracy. The R2, RMSE, and mean absolute difference (MAD) of the predictive model for the number of leaves were 0.94, 0.87, and 0.88, respectively, and the R2 and RMSE of the model for estimating the plant age were 0.92 and 0.29 weeks, respectively. The models developed in this study can be utilized in high-throughput phenotyping systems for growth monitoring and analysis of phenotypic traits during watermelon cultivation.
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Affiliation(s)
- Shona Nabwire
- Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, South Korea
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, South Korea
| | | | | | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, South Korea
- Department of Smart Agriculture Systems, Chungnam National University, Daejeon, South Korea
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17
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Li Y, Zaheri S, Nguyen K, Liu L, Hassanipour F, Pace BS, Bleris L. Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations. Sci Rep 2022; 12:1481. [PMID: 35087158 PMCID: PMC8795181 DOI: 10.1038/s41598-022-05575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/17/2021] [Indexed: 11/08/2022] Open
Abstract
Two common hemoglobinopathies, sickle cell disease (SCD) and β-thalassemia, arise from genetic mutations within the β-globin gene. In this work, we identified a 500-bp motif (Fetal Chromatin Domain, FCD) upstream of human ϒ-globin locus and showed that the removal of this motif using CRISPR technology reactivates the expression of ϒ-globin. Next, we present two different cell morphology-based machine learning approaches that can be used identify human blood cells (KU-812) that harbor CRISPR-mediated FCD genetic modifications. Three candidate models from the first approach, which uses multilayer perceptron algorithm (MLP 20-26, MLP26-18, and MLP 30-26) and flow cytometry-derived cellular data, yielded 0.83 precision, 0.80 recall, 0.82 accuracy, and 0.90 area under the ROC (receiver operating characteristic) curve when predicting the edited cells. In comparison, the candidate model from the second approach, which uses deep learning (T2D5) and DIC microscopy-derived imaging data, performed with less accuracy (0.80) and ROC AUC (0.87). We envision that equivalent machine learning-based models can complement currently available genotyping protocols for specific genetic modifications which result in morphological changes in human cells.
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Affiliation(s)
- Yi Li
- Bioengineering Department, The University of Texas at Dallas, Richardson, TX, USA.
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA.
| | - Shadi Zaheri
- Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Khai Nguyen
- Bioengineering Department, The University of Texas at Dallas, Richardson, TX, USA
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA
| | - Li Liu
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Fatemeh Hassanipour
- Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Betty S Pace
- Department of Pediatrics, Augusta University, Augusta, GA, USA
| | - Leonidas Bleris
- Bioengineering Department, The University of Texas at Dallas, Richardson, TX, USA.
- Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, USA.
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA.
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18
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Egan LM, Conaty WC, Stiller WN. Core Collections: Is There Any Value for Cotton Breeding? FRONTIERS IN PLANT SCIENCE 2022; 13:895155. [PMID: 35574064 PMCID: PMC9096653 DOI: 10.3389/fpls.2022.895155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 04/06/2022] [Indexed: 05/08/2023]
Abstract
Global plant breeding activities are reliant on the available genetic variation held in extant varieties and germplasm collections. Throughout the mid- to late 1900s, germplasm collecting efforts were prioritized for breeding programs to archive precious material before it disappeared and led to the development of the numerous large germplasm resources now available in different countries. In recent decades, however, the maintenance and particularly the expansion of these germplasm resources have come under threat, and there has been a significant decline in investment in further collecting expeditions, an increase in global biosecurity restrictions, and restrictions placed on the open exchange of some commercial germplasm between breeders. The large size of most genebank collections, as well as constraints surrounding the availability and reliability of accurate germplasm passport data and physical or genetic characterization of the accessions in collections, limits germplasm utilization by plant breeders. To overcome these constraints, core collections, defined as a representative subset of the total germplasm collection, have gained popularity. Core collections aim to increase germplasm utilization by containing highly characterized germplasm that attempts to capture the majority of the variation in a whole collection. With the recent availability of many new genetic tools, the potential to unlock the value of these resources can now be realized. The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program supplies 100% of the cotton cultivars grown in Australia. The program is reliant on the use of plant genetic resources for the development of improved cotton varieties to address emerging challenges in pest and disease resistance as well as the global changes occurring in the climate. Currently, the CSIRO germplasm collection is actively maintained but underutilized by plant breeders. This review presents an overview of the Australian cotton germplasm resources and discusses the appropriateness of a core collection for cotton breeding programs.
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Sohn SI, Pandian S, Oh YJ, Zaukuu JLZ, Kang HJ, Ryu TH, Cho WS, Cho YS, Shin EK, Cho BK. An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms. Int J Mol Sci 2021; 22:ijms22189940. [PMID: 34576101 PMCID: PMC8469702 DOI: 10.3390/ijms22189940] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 01/12/2023] Open
Abstract
Near-infrared spectroscopy (NIRS) has become a more popular approach for quantitative and qualitative analysis of feeds, foods and medicine in conjunction with an arsenal of chemometric tools. This was the foundation for the increased importance of NIRS in other fields, like genetics and transgenic monitoring. A considerable number of studies have utilized NIRS for the effective identification and discrimination of plants and foods, especially for the identification of genetically modified crops. Few previous reviews have elaborated on the applications of NIRS in agriculture and food, but there is no comprehensive review that compares the use of NIRS in the detection of genetically modified organisms (GMOs). This is particularly important because, in comparison to previous technologies such as PCR and ELISA, NIRS offers several advantages, such as speed (eliminating time-consuming procedures), non-destructive/non-invasive analysis, and is inexpensive in terms of cost and maintenance. More importantly, this technique has the potential to measure multiple quality components in GMOs with reliable accuracy. In this review, we brief about the fundamentals and versatile applications of NIRS for the effective identification of GMOs in the agricultural and food systems.
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Affiliation(s)
- Soo-In Sohn
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
- Correspondence: (S.-I.S.); (B.-K.C.)
| | - Subramani Pandian
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Young-Ju Oh
- Institute for Future Environmental Ecology Co., Ltd., Jeonju 54883, Korea;
| | - John-Lewis Zinia Zaukuu
- Department of Measurements and Process Control, Szent István University, H-1118 Budapest, Hungary;
| | - Hyeon-Jung Kang
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Tae-Hun Ryu
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Woo-Suk Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Youn-Sung Cho
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Eun-Kyoung Shin
- Department of Agricultural Biotechnology, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 54874, Korea; (S.P.); (H.-J.K.); (T.-H.R.); (W.-S.C.); (Y.-S.C.); (E.-K.S.)
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Korea
- Correspondence: (S.-I.S.); (B.-K.C.)
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