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Cheng X, Zhou G, Chen W, Tan L, Long Q, Cui F, Tan L, Zou G, Tan Y. Current status of molecular rice breeding for durable and broad-spectrum resistance to major diseases and insect pests. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:219. [PMID: 39254868 PMCID: PMC11387466 DOI: 10.1007/s00122-024-04729-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Accepted: 08/24/2024] [Indexed: 09/11/2024]
Abstract
In the past century, there have been great achievements in identifying resistance (R) genes and quantitative trait loci (QTLs) as well as revealing the corresponding molecular mechanisms for resistance in rice to major diseases and insect pests. The introgression of R genes to develop resistant rice cultivars has become the most effective and eco-friendly method to control pathogens/insects at present. However, little attention has been paid to durable and broad-spectrum resistance, which determines the real applicability of R genes. Here, we summarize all the R genes and QTLs conferring durable and broad-spectrum resistance in rice to fungal blast, bacterial leaf blight (BLB), and the brown planthopper (BPH) in molecular breeding. We discuss the molecular mechanisms and feasible methods of improving durable and broad-spectrum resistance to blast, BLB, and BPH. We will particularly focus on pyramiding multiple R genes or QTLs as the most useful method to improve durability and broaden the disease/insect spectrum in practical breeding regardless of its uncertainty. We believe that this review provides useful information for scientists and breeders in rice breeding for multiple stress resistance in the future.
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Affiliation(s)
- Xiaoyan Cheng
- Jiangxi Tiandao Liangan Seed Industry Co., Ltd., 568 South Huancheng Rd., Yuanzhou Dist., Yichun, People's Republic of China
- National Engineering Research Center of Rice (Nanchang), Rice Research Institute, Jiangxi Academy of Agricultural Sciences, Nanchang, People's Republic of China
- College of Life Sciences and Resources and Environment, Yichun University, Yichun, People's Republic of China
| | - Guohua Zhou
- College of Life Sciences and Resources and Environment, Yichun University, Yichun, People's Republic of China
| | - Wei Chen
- Jiangxi Super-Rice Research and Development Center, Jiangxi Provincial Key Laboratory of Rice Germplasm Innovation and Breeding, Jiangxi Academy of Agricultural Sciences, National Engineering Research Center for Rice, Nanchang, People's Republic of China
| | - Lin Tan
- Jiangxi Tiandao Liangan Seed Industry Co., Ltd., 568 South Huancheng Rd., Yuanzhou Dist., Yichun, People's Republic of China
| | - Qishi Long
- Jiangxi Tiandao Liangan Seed Industry Co., Ltd., 568 South Huancheng Rd., Yuanzhou Dist., Yichun, People's Republic of China
| | - Fusheng Cui
- Yichun Academy of Sciences (Jiangxi Selenium-Rich Industry Research Institute), Yichun, People's Republic of China
| | - Lei Tan
- Jiangxi Tiandao Liangan Seed Industry Co., Ltd., 568 South Huancheng Rd., Yuanzhou Dist., Yichun, People's Republic of China
| | - Guoxing Zou
- National Engineering Research Center of Rice (Nanchang), Rice Research Institute, Jiangxi Academy of Agricultural Sciences, Nanchang, People's Republic of China.
| | - Yong Tan
- Jiangxi Tiandao Liangan Seed Industry Co., Ltd., 568 South Huancheng Rd., Yuanzhou Dist., Yichun, People's Republic of China.
- Jiangxi Super-Rice Research and Development Center, Jiangxi Provincial Key Laboratory of Rice Germplasm Innovation and Breeding, Jiangxi Academy of Agricultural Sciences, National Engineering Research Center for Rice, Nanchang, People's Republic of China.
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Resende RT, Hickey L, Amaral CH, Peixoto LL, Marcatti GE, Xu Y. Satellite-enabled enviromics to enhance crop improvement. MOLECULAR PLANT 2024; 17:848-866. [PMID: 38637991 DOI: 10.1016/j.molp.2024.04.005] [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: 11/15/2023] [Revised: 04/04/2024] [Accepted: 04/11/2024] [Indexed: 04/20/2024]
Abstract
Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
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Affiliation(s)
- Rafael T Resende
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil; TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil.
| | - Lee Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Cibele H Amaral
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA; Environmental Data Science Innovation & Inclusion Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, USA
| | - Lucas L Peixoto
- Universidade Federal de Goiás (UFG), Agronomy Department, Plant Breeding Sector, Goiânia (GO) 74690-900, Brazil
| | - Gustavo E Marcatti
- TheCROP, a Precision-Breeding Startup: Enviromics, Phenomics, and Genomics, No Zip-code, Operating Virtually, Goiânia (GO) and Sete Lagoas (MG), Brazil; Universidade Federal de São João del-Rei, Forest Engineering Department, Campus Sete Lagoas, Sete Lagoas (MG) 35701-970, Brazil
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China; BGI Bioverse, Shenzhen 518083, China.
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Mohan I, Joshi B, Pathania D, Dhar S, Bhau BS. Phytobial remediation advances and application of omics and artificial intelligence: a review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:37988-38021. [PMID: 38780844 DOI: 10.1007/s11356-024-33690-3] [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: 05/19/2023] [Accepted: 05/11/2024] [Indexed: 05/25/2024]
Abstract
Industrialization and urbanization increased the use of chemicals in agriculture, vehicular emissions, etc., and spoiled all environmental sectors. It causes various problems among living beings at multiple levels and concentrations. Phytoremediation and microbial association are emerging as a potential method for removing heavy metals and other contaminants from soil. The treatment uses plant physiology and metabolism to remove or clean up various soil contaminants efficiently. In recent years, omics and artificial intelligence have been seen as powerful techniques for phytobial remediation. Recently, AI and modeling are used to analyze large data generated by omics technologies. Machine learning algorithms can be used to develop predictive models that can help guide the selection of the most appropriate plant and plant growth-promoting rhizobacteria combination that is most effective at remediation. In this review, emphasis is given to the phytoremediation techniques being explored worldwide in soil contamination.
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Affiliation(s)
- Indica Mohan
- Department of Environmental Sciences, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
- Department of Botany, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
| | - Babita Joshi
- Plant Molecular Genetics Laboratory, CSIR-National Botanical Research Institute, Rana Pratap Marg, Lucknow, U.P., 226001, India
| | - Deepak Pathania
- Department of Environmental Sciences, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
- Department of Botany, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
| | - Sunil Dhar
- Department of Environmental Sciences, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
- Department of Botany, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India
| | - Brijmohan Singh Bhau
- Department of Botany, Central University of Jammu, Rahya-Suchani, Bagla, District Samba, Jammu and Kashmir, 181143, India.
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Raza A, Salehi H, Bashir S, Tabassum J, Jamla M, Charagh S, Barmukh R, Mir RA, Bhat BA, Javed MA, Guan DX, Mir RR, Siddique KHM, Varshney RK. Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity. PLANT CELL REPORTS 2024; 43:80. [PMID: 38411713 PMCID: PMC10899315 DOI: 10.1007/s00299-024-03153-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 02/28/2024]
Abstract
The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.
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Affiliation(s)
- Ali Raza
- Guangdong Key Laboratory of Plant Epigenetics, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 518060, China
| | - Hajar Salehi
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122, Piacenza, Italy
| | - Shanza Bashir
- Institute of Environmental Sciences and Engineering, School of Civil and Environmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Javaria Tabassum
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Monica Jamla
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune, 411016, India
| | - Sidra Charagh
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Hangzhou, China
| | - Rutwik Barmukh
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia
| | - Rakeeb Ahmad Mir
- Department of Biotechnology, School of Life Sciences, Central University of Kashmir, Ganderbal, India
| | - Basharat Ahmad Bhat
- Department of Bio-Resources, Amar Singh College Campus, Cluster University Srinagar, Srinagar, JK, India
| | - Muhammad Arshad Javed
- Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences, University of the Punjab, Lahore, Pakistan
| | - Dong-Xing Guan
- Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Faculty of Agriculture, Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), Srinagar, Kashmir, India
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, Australia.
| | - Rajeev K Varshney
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia.
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Biswas A, Kumari A, Gaikwad DS, Pandey DK. Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2023; 27:550-569. [PMID: 38100404 DOI: 10.1089/omi.2023.0197] [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: 12/17/2023]
Abstract
With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.
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Affiliation(s)
- Aakanksha Biswas
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - Aditi Kumari
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
| | - D S Gaikwad
- Amity Institute of Organic Agriculture, Amity University, Noida, India
| | - Dhananjay K Pandey
- Amity Institute of Biotechnology, Amity University Jharkhand, Ranchi, India
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Sahu SK, Waseem M, Aslam MM. Editorial: Bioinformatics, big data and agriculture: a challenge for the future. FRONTIERS IN PLANT SCIENCE 2023; 14:1271305. [PMID: 37908837 PMCID: PMC10614287 DOI: 10.3389/fpls.2023.1271305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/04/2023] [Indexed: 11/02/2023]
Affiliation(s)
- Sunil Kumar Sahu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Muhammad Waseem
- School of Tropical Agriculture and Forestry (School of Agriculture and Rural Affairs, School of Rural Revitalization), Hainan University, Haikou, Hainan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Researh, Sanya, China
- Fang Zhiyuan Academician Team Innovation Center of Hainan Province, Haikou, China
| | - Mehtab Muhammad Aslam
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, China
- College of Agriculture, Food and Natural Resources (CAFNR), Division of Plant Sciences & Technology, University of Missouri, Columbia, MO, United States
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Fu H, Yang Y. How Plants Tolerate Salt Stress. Curr Issues Mol Biol 2023; 45:5914-5934. [PMID: 37504290 PMCID: PMC10378706 DOI: 10.3390/cimb45070374] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 07/13/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023] Open
Abstract
Soil salinization inhibits plant growth and seriously restricts food security and agricultural development. Excessive salt can cause ionic stress, osmotic stress, and ultimately oxidative stress in plants. Plants exclude excess salt from their cells to help maintain ionic homeostasis and stimulate phytohormone signaling pathways, thereby balancing growth and stress tolerance to enhance their survival. Continuous innovations in scientific research techniques have allowed great strides in understanding how plants actively resist salt stress. Here, we briefly summarize recent achievements in elucidating ionic homeostasis, osmotic stress regulation, oxidative stress regulation, and plant hormonal responses under salt stress. Such achievements lay the foundation for a comprehensive understanding of plant salt-tolerance mechanisms.
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Affiliation(s)
- Haiqi Fu
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
- Tianjin Key Laboratory of Crop Genetics and Breeding, Institute of Crop Sciences, Tianjin Academy of Agricultural Sciences, Tianjin 300380, China
| | - Yongqing Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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Cortés AJ, Du H. Molecular Genetics Enhances Plant Breeding. Int J Mol Sci 2023; 24:9977. [PMID: 37373125 DOI: 10.3390/ijms24129977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Human-driven plant selection, a practice as ancient as agriculture itself, has laid the foundations of plant breeding and contemporary farming [...].
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Affiliation(s)
- Andrés J Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 vía Rionegro-Las Palmas, Rionegro 054048, Colombia
| | - Hai Du
- College of Agronomy and Biotechnology, Chongqing Engineering Research Center for Rapeseed, Southwest University, Chongqing 400000, China
- Integrative Science Center of Germplasm Creation in Western China (Chongqing) Science City and Southwest University, of Agronomy and Biotechnology, Southwest University, Chongqing 400000, China
- Engineering Research Center, South Upland Agriculture, Ministry of Education, Chongqing 400000, China
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Xu L, Cao B, Ning S, Zhang W, Zhao F. Peanut leaf disease identification with deep learning algorithms. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:25. [PMID: 37313521 PMCID: PMC10248705 DOI: 10.1007/s11032-023-01370-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/11/2023] [Indexed: 06/15/2023]
Abstract
Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%-23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01370-8.
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Affiliation(s)
- Laixiang Xu
- School of Information and Communication Engineering, Hainan University, 570228 Haikou, China
- Haikou, China
| | - Bingxu Cao
- Information Engineering Department, Luohe Vocational Technology College, Luohe, 462000 China
- Luohe, China
| | - Shiyuan Ning
- Department of Software Information, China Electronics Technology Group Corporation 36th Research Institute, Jiaxing, 314033 China
| | - Wenbo Zhang
- School of Information and Communication Engineering, Hainan University, 570228 Haikou, China
| | - Fengjie Zhao
- Henan Sui Xian People’s Hospital, The First Affiliated Hospital of Zhengzhou University, Shangqiu First People’s Hospital, Shangqiu, 476000 China
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