Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects
Against the backdrop of a growing global population and intensifying climate change, crop pests and diseases have become significant challenges affecting agricultural production and food security. Efficient and precise detection and control of crop pests and diseases are crucial for ensuring yield a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/6/1416 |
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| author | Jiaxing Xie Meiyi Lu Qunpeng Gao Liye Chen Yingxin Zou Jiatao Wu Yue Cao Niechong Xu Weixing Wang Jun Li |
| author_facet | Jiaxing Xie Meiyi Lu Qunpeng Gao Liye Chen Yingxin Zou Jiatao Wu Yue Cao Niechong Xu Weixing Wang Jun Li |
| author_sort | Jiaxing Xie |
| collection | DOAJ |
| description | Against the backdrop of a growing global population and intensifying climate change, crop pests and diseases have become significant challenges affecting agricultural production and food security. Efficient and precise detection and control of crop pests and diseases are crucial for ensuring yield and quality, reducing agricultural losses, and promoting sustainable agriculture. In recent years, intelligent diagnostic methods based on machine learning and deep learning have advanced rapidly, providing new technological means for the early detection and management of crop pests and diseases. Meanwhile, large language models have demonstrated potential advantages in information integration and knowledge inference, offering prospects for more scientific and efficient decision support in pest and disease control. This paper reviews the research progress in the application of machine learning, deep learning, and large language models in crop pest and disease detection and control, analyzes the challenges in current technological implementations, and explores future development directions. |
| format | Article |
| id | doaj-art-5ab970d3f0064eb5b1656c4e0d9d1559 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-5ab970d3f0064eb5b1656c4e0d9d15592025-08-20T02:24:30ZengMDPI AGAgronomy2073-43952025-06-01156141610.3390/agronomy15061416Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future ProspectsJiaxing Xie0Meiyi Lu1Qunpeng Gao2Liye Chen3Yingxin Zou4Jiatao Wu5Yue Cao6Niechong Xu7Weixing Wang8Jun Li9State Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaZhujiang College, South China Agricultural University, Guangzhou 510900, ChinaState Key Laboratory of Agricultural Equipment Technology, South China Agricultural University, Guangzhou 510642, ChinaAgainst the backdrop of a growing global population and intensifying climate change, crop pests and diseases have become significant challenges affecting agricultural production and food security. Efficient and precise detection and control of crop pests and diseases are crucial for ensuring yield and quality, reducing agricultural losses, and promoting sustainable agriculture. In recent years, intelligent diagnostic methods based on machine learning and deep learning have advanced rapidly, providing new technological means for the early detection and management of crop pests and diseases. Meanwhile, large language models have demonstrated potential advantages in information integration and knowledge inference, offering prospects for more scientific and efficient decision support in pest and disease control. This paper reviews the research progress in the application of machine learning, deep learning, and large language models in crop pest and disease detection and control, analyzes the challenges in current technological implementations, and explores future development directions.https://www.mdpi.com/2073-4395/15/6/1416crop pests and diseases detectionpest and disease controlmachine learningdeep learninglarge language models |
| spellingShingle | Jiaxing Xie Meiyi Lu Qunpeng Gao Liye Chen Yingxin Zou Jiatao Wu Yue Cao Niechong Xu Weixing Wang Jun Li Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects Agronomy crop pests and diseases detection pest and disease control machine learning deep learning large language models |
| title | Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects |
| title_full | Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects |
| title_fullStr | Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects |
| title_full_unstemmed | Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects |
| title_short | Intelligent Detection and Control of Crop Pests and Diseases: Current Status and Future Prospects |
| title_sort | intelligent detection and control of crop pests and diseases current status and future prospects |
| topic | crop pests and diseases detection pest and disease control machine learning deep learning large language models |
| url | https://www.mdpi.com/2073-4395/15/6/1416 |
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