A Review of Computer Vision and Deep Learning Applications in Crop Growth Management
Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smar...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8438 |
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| author | Zhijie Cao Shantong Sun Xu Bao |
| author_facet | Zhijie Cao Shantong Sun Xu Bao |
| author_sort | Zhijie Cao |
| collection | DOAJ |
| description | Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. |
| format | Article |
| id | doaj-art-de785c8519a24ca5a07d5937bcbfbb77 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-de785c8519a24ca5a07d5937bcbfbb772025-08-20T03:36:35ZengMDPI AGApplied Sciences2076-34172025-07-011515843810.3390/app15158438A Review of Computer Vision and Deep Learning Applications in Crop Growth ManagementZhijie Cao0Shantong Sun1Xu Bao2School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaAgriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture.https://www.mdpi.com/2076-3417/15/15/8438agricultural applicationscomputer visiondeep learningtarget recognitioncrop gradingdisease monitoring |
| spellingShingle | Zhijie Cao Shantong Sun Xu Bao A Review of Computer Vision and Deep Learning Applications in Crop Growth Management Applied Sciences agricultural applications computer vision deep learning target recognition crop grading disease monitoring |
| title | A Review of Computer Vision and Deep Learning Applications in Crop Growth Management |
| title_full | A Review of Computer Vision and Deep Learning Applications in Crop Growth Management |
| title_fullStr | A Review of Computer Vision and Deep Learning Applications in Crop Growth Management |
| title_full_unstemmed | A Review of Computer Vision and Deep Learning Applications in Crop Growth Management |
| title_short | A Review of Computer Vision and Deep Learning Applications in Crop Growth Management |
| title_sort | review of computer vision and deep learning applications in crop growth management |
| topic | agricultural applications computer vision deep learning target recognition crop grading disease monitoring |
| url | https://www.mdpi.com/2076-3417/15/15/8438 |
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