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Solutions and challenges in AI-based pest and disease recognition

文献类型：期刊论文

作者Liu, Xinda1; Zhang, Qinyu1; Min, Weiqing2,3; Geng, Guohua1; Jiang, Shuqiang2,3
刊名COMPUTERS AND ELECTRONICS IN AGRICULTURE
出版日期2025-11-01
卷号238页码:22
关键词Agricultural practices Crop diseases and pets Deep learning Few-shot learning Network architectures Lightweight models Hardware
ISSN号0168-1699
DOI10.1016/j.compag.2025.110775
英文摘要The global food crisis, exacerbated by the intensification of crop diseases and pests, poses a significant threat to food security and nutrition. Currently, approximately 350 million people are experiencing extreme hunger, and this number is projected to rise to 943 million by 2025. Consequently, there is an urgent need for effective pest and disease management strategies in agriculture. Traditional identification methods are limited by accuracy, cost, and dependence on human expertise, which hinders timely and efficient pest and disease control. This study investigates the potential of artificial intelligence, particularly deep learning techniques, to enhance the detection and classification of plant diseases and pests. The research focuses on addressing four main challenges: data scarcity, outdated network architectures, computational constraints of terminal devices, and resource and compatibility issues. This paper reviews recent advancements in AI technologies, including few-shot learning, innovative training methods and network architectures, lightweight models, as well as deployment and hardware technologies. Additionally, it discusses the integration of AI in agriculture, highlighting the importance of few-shot learning and the application of new technologies such as Generative Adversarial Networks and Transformers in enhancing pest and disease identification. By providing a comprehensive review of state-of-the-art methods and identifying the unique value of AI in revolutionizing agricultural practices, increasing efficiency, and promoting sustainability, this study makes a significant contribution to the field.
资助项目National Key R&D Program of China[2023YFF0906504] ; National Natural Science Foundation of China[62271393] ; Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University[VRLAB2024C02] ; General Projects of the Shaanxi Provincial Department of Science and Technology[2025JC-YBQN-801] ; General Projects of the Shaanxi Provincial Education Depart-ment Research Program[24JK0675]
WOS研究方向Agriculture ; Computer Science
语种英语
WOS记录号WOS:001544965000002
出版者ELSEVIER SCI LTD
源URL[http://119.78.100.204/handle/2XEOYT63/41982]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Min, Weiqing
作者单位1.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China
2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Xinda,Zhang, Qinyu,Min, Weiqing,et al. Solutions and challenges in AI-based pest and disease recognition[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2025,238:22.
APA Liu, Xinda,Zhang, Qinyu,Min, Weiqing,Geng, Guohua,&Jiang, Shuqiang.(2025).Solutions and challenges in AI-based pest and disease recognition.COMPUTERS AND ELECTRONICS IN AGRICULTURE,238,22.
MLA Liu, Xinda,et al."Solutions and challenges in AI-based pest and disease recognition".COMPUTERS AND ELECTRONICS IN AGRICULTURE 238(2025):22.

入库方式： OAI收割

来源：计算技术研究所

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