Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection
Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, b...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1435016/full |
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| author | Hongyan Zhu Hongyan Zhu Chengzhi Lin Chengzhi Lin Gengqi Liu Gengqi Liu Dani Wang Dani Wang Shuai Qin Shuai Qin Anjie Li Anjie Li Jun-Li Xu Yong He |
| author_facet | Hongyan Zhu Hongyan Zhu Chengzhi Lin Chengzhi Lin Gengqi Liu Gengqi Liu Dani Wang Dani Wang Shuai Qin Shuai Qin Anjie Li Anjie Li Jun-Li Xu Yong He |
| author_sort | Hongyan Zhu |
| collection | DOAJ |
| description | Controlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future. |
| format | Article |
| id | doaj-art-afc62b0c8f924deda02fa80951ce3dbe |
| institution | OA Journals |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-afc62b0c8f924deda02fa80951ce3dbe2025-08-20T01:54:16ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-10-011510.3389/fpls.2024.14350161435016Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detectionHongyan Zhu0Hongyan Zhu1Chengzhi Lin2Chengzhi Lin3Gengqi Liu4Gengqi Liu5Dani Wang6Dani Wang7Shuai Qin8Shuai Qin9Anjie Li10Anjie Li11Jun-Li Xu12Yong He13Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaGuangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaGuangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaGuangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaGuangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaGuangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, ChinaKey Laboratory of Integrated Circuits and Microsystems (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, ChinaSchool of Biosystems and Food Engineering, University College Dublin, Dublin, IrelandCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, ChinaControlling crop diseases and pests is essential for intelligent agriculture (IA) due to the significant reduction in crop yield and quality caused by these problems. In recent years, the remote sensing (RS) areas has been prevailed over by unmanned aerial vehicle (UAV)-based applications. Herein, by using methods such as keyword co-contribution analysis and author co-occurrence analysis in bibliometrics, we found out the hot-spots of this field. UAV platforms equipped with various types of cameras and other advanced sensors, combined with artificial intelligence (AI) algorithms, especially for deep learning (DL) were reviewed. Acknowledging the critical role of comprehending crop diseases and pests, along with their defining traits, we provided a concise overview as indispensable foundational knowledge. Additionally, some widely used traditional machine learning (ML) algorithms were presented and the performance results were tabulated to form a comparison. Furthermore, we summarized crop diseases and pests monitoring techniques using DL and introduced the application for prediction and classification. Take it a step further, the newest and the most concerned applications of large language model (LLM) and large vision model (LVM) in agriculture were also mentioned herein. At the end of this review, we comprehensively discussed some deficiencies in the existing research and some challenges to be solved, as well as some practical solutions and suggestions in the near future.https://www.frontiersin.org/articles/10.3389/fpls.2024.1435016/fullintelligent agriculture (IA)deep learning (DL)crop diseases and pestsremote sensing (RS)unmanned aerial vehicle (UAV) |
| spellingShingle | Hongyan Zhu Hongyan Zhu Chengzhi Lin Chengzhi Lin Gengqi Liu Gengqi Liu Dani Wang Dani Wang Shuai Qin Shuai Qin Anjie Li Anjie Li Jun-Li Xu Yong He Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection Frontiers in Plant Science intelligent agriculture (IA) deep learning (DL) crop diseases and pests remote sensing (RS) unmanned aerial vehicle (UAV) |
| title | Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection |
| title_full | Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection |
| title_fullStr | Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection |
| title_full_unstemmed | Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection |
| title_short | Intelligent agriculture: deep learning in UAV-based remote sensing imagery for crop diseases and pests detection |
| title_sort | intelligent agriculture deep learning in uav based remote sensing imagery for crop diseases and pests detection |
| topic | intelligent agriculture (IA) deep learning (DL) crop diseases and pests remote sensing (RS) unmanned aerial vehicle (UAV) |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1435016/full |
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