Applications of machine learning and deep learning in agriculture: A comprehensive review
The digitalization of agriculture has increasingly integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) to address the challenges arising from population growth, climate change (CC), and resource limitations. This study provides a comprehensive review of the potenti...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-07-01
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| Series: | Green Technologies and Sustainability |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949736125000338 |
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| author | Muhammad Waqas Adila Naseem Usa Wannasingha Humphries Phyo Thandar Hlaing Porntip Dechpichai Angkool Wangwongchai |
| author_facet | Muhammad Waqas Adila Naseem Usa Wannasingha Humphries Phyo Thandar Hlaing Porntip Dechpichai Angkool Wangwongchai |
| author_sort | Muhammad Waqas |
| collection | DOAJ |
| description | The digitalization of agriculture has increasingly integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) to address the challenges arising from population growth, climate change (CC), and resource limitations. This study provides a comprehensive review of the potential applications of AI techniques across various stages of agricultural production, with a particular focus on innovations that align with climate-smart agricultural practices. The review encompasses research conducted from 2018–2024, emphasizing the use of ML and DL in areas such as crop selection, land monitoring and management, water, soil and nutrient management, weed control, harvest and post-harvest practices, pest and insect management, and soil management. The findings underscore that ML and DL facilitate the analysis of complex datasets, enabling data-driven decision-making, reducing reliance on subjective expertise, and improving farm management strategies. Despite challenges such as data availability, model interpretability, scalability, security concerns, and user interface design, which hinder the widespread adoption of ML and DL methodologies, collaborative efforts among stakeholders can help overcome these barriers. This review concludes that ongoing advancements in ML and DL present significant opportunities to enhance agricultural productivity, sustainability, and resilience. By leveraging data-driven insights and innovative technologies, the agricultural sector can transition toward more efficient, environmentally sustainable, and economically viable practices, contributing to global food security and environmental preservation. |
| format | Article |
| id | doaj-art-64d720e165bc4beab0feec201434283f |
| institution | Kabale University |
| issn | 2949-7361 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Green Technologies and Sustainability |
| spelling | doaj-art-64d720e165bc4beab0feec201434283f2025-08-20T03:28:06ZengKeAi Communications Co., Ltd.Green Technologies and Sustainability2949-73612025-07-013310019910.1016/j.grets.2025.100199Applications of machine learning and deep learning in agriculture: A comprehensive reviewMuhammad Waqas0Adila Naseem1Usa Wannasingha Humphries2Phyo Thandar Hlaing3Porntip Dechpichai4Angkool Wangwongchai5The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation, Bangkok, ThailandDepartment of Food Science, Faculty of Agriculture, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand; Department of Food Science, Bahauddin Zakariya University, 60000, Multan, PakistanDepartment of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Corresponding author.The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, 10140, Thailand; Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation, Bangkok, ThailandDepartment of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, 10140, ThailandDepartment of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, 10140, ThailandThe digitalization of agriculture has increasingly integrated artificial intelligence (AI), machine learning (ML), and deep learning (DL) to address the challenges arising from population growth, climate change (CC), and resource limitations. This study provides a comprehensive review of the potential applications of AI techniques across various stages of agricultural production, with a particular focus on innovations that align with climate-smart agricultural practices. The review encompasses research conducted from 2018–2024, emphasizing the use of ML and DL in areas such as crop selection, land monitoring and management, water, soil and nutrient management, weed control, harvest and post-harvest practices, pest and insect management, and soil management. The findings underscore that ML and DL facilitate the analysis of complex datasets, enabling data-driven decision-making, reducing reliance on subjective expertise, and improving farm management strategies. Despite challenges such as data availability, model interpretability, scalability, security concerns, and user interface design, which hinder the widespread adoption of ML and DL methodologies, collaborative efforts among stakeholders can help overcome these barriers. This review concludes that ongoing advancements in ML and DL present significant opportunities to enhance agricultural productivity, sustainability, and resilience. By leveraging data-driven insights and innovative technologies, the agricultural sector can transition toward more efficient, environmentally sustainable, and economically viable practices, contributing to global food security and environmental preservation.http://www.sciencedirect.com/science/article/pii/S2949736125000338Machine learningDeep learningAgricultureCrop managementYield predictionSustainable agriculture |
| spellingShingle | Muhammad Waqas Adila Naseem Usa Wannasingha Humphries Phyo Thandar Hlaing Porntip Dechpichai Angkool Wangwongchai Applications of machine learning and deep learning in agriculture: A comprehensive review Green Technologies and Sustainability Machine learning Deep learning Agriculture Crop management Yield prediction Sustainable agriculture |
| title | Applications of machine learning and deep learning in agriculture: A comprehensive review |
| title_full | Applications of machine learning and deep learning in agriculture: A comprehensive review |
| title_fullStr | Applications of machine learning and deep learning in agriculture: A comprehensive review |
| title_full_unstemmed | Applications of machine learning and deep learning in agriculture: A comprehensive review |
| title_short | Applications of machine learning and deep learning in agriculture: A comprehensive review |
| title_sort | applications of machine learning and deep learning in agriculture a comprehensive review |
| topic | Machine learning Deep learning Agriculture Crop management Yield prediction Sustainable agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S2949736125000338 |
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