Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review
Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a pro...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/698 |
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| author | Shaohua Wang Dachuan Xu Haojian Liang Yongqing Bai Xiao Li Junyuan Zhou Cheng Su Wenyu Wei |
| author_facet | Shaohua Wang Dachuan Xu Haojian Liang Yongqing Bai Xiao Li Junyuan Zhou Cheng Su Wenyu Wei |
| author_sort | Shaohua Wang |
| collection | DOAJ |
| description | Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture. |
| format | Article |
| id | doaj-art-4258d07c49ba4330b20d2d68420f60d1 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4258d07c49ba4330b20d2d68420f60d12025-08-20T02:03:32ZengMDPI AGRemote Sensing2072-42922025-02-0117469810.3390/rs17040698Advances in Deep Learning Applications for Plant Disease and Pest Detection: A ReviewShaohua Wang0Dachuan Xu1Haojian Liang2Yongqing Bai3Xiao Li4Junyuan Zhou5Cheng Su6Wenyu Wei7Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaTraditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.https://www.mdpi.com/2072-4292/17/4/698deep learningdisease detectionplant diseases and pestsimage classificationobject detectionconvolutional neural network |
| spellingShingle | Shaohua Wang Dachuan Xu Haojian Liang Yongqing Bai Xiao Li Junyuan Zhou Cheng Su Wenyu Wei Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review Remote Sensing deep learning disease detection plant diseases and pests image classification object detection convolutional neural network |
| title | Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review |
| title_full | Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review |
| title_fullStr | Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review |
| title_full_unstemmed | Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review |
| title_short | Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review |
| title_sort | advances in deep learning applications for plant disease and pest detection a review |
| topic | deep learning disease detection plant diseases and pests image classification object detection convolutional neural network |
| url | https://www.mdpi.com/2072-4292/17/4/698 |
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