Solutions and challenges in AI-based pest and disease recognition

doi:10.1016/j.compag.2025.110775

	Solutions and challenges in AI-based pest and disease recognition
	Liu, Xinda 1; Zhang, Qinyu 1; Min, Weiqing 2,3; Geng, Guohua 1; Jiang, Shuqiang 2,3
	2025-11-01
发表期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
卷号	238 页码:22
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 The research focuses on addressing four main challenges: data scarcity, outdated network architectures, computational constraints of terminal devices, and resource and compatibility issues.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.
关键词	Agricultural practices Crop diseases and pets Deep learning Few-shot learning Network architectures Lightweight models Hardware
DOI	10.1016/j.compag.2025.110775
收录类别	SCI
语种	英语
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
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001544965000002
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41983
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.