Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture

作     者：Upadhyay, Abhishek Chandel, Narendra Singh Singh, Krishna Pratap Chakraborty, Subir Kumar Nandede, Balaji M. Kumar, Mohit Subeesh, A. Upendar, Konga Salem, Ali Elbeltagi, Ahmed 

作者机构：ICAR Cent Inst Agr Engn Dept Farm Machinery & Power Engn Bhopal 462038 Madhya Pradesh India ICAR Cent Inst Agr Engn Agr Mechanizat Div Bhopal 462038 Madhya Pradesh India Indian Council Agr Res New Delhi 110001 India ICAR Cent Inst Agr Engn Technol Transfer Div Bhopal 462038 Madhya Pradesh India Southern Reg Farm Machinery Training & Testing Ins Anantapur Andhra Pradesh India Sri Karan Narendra Agr Univ Jaipur 303329 Rajasthan India Infyz Solut Hyderabad Telangana India Minia Univ Fac Engn Civil Engn Dept Al Minya Egypt Univ Pecs Fac Engn & Informat Technol Struct Diagnost & Anal Res Grp Pecs Hungary Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt 

出 版 物：《ARTIFICIAL INTELLIGENCE REVIEW》 (Artif Intell Rev)

年 卷 期：2025年第58卷第3期

页      面：1-64页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：University of Pcs 

主　　题：Computer vision Deep learning Plant diseases detection Vision transformers Generative adversarial networks Vision language models 

摘      要：Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.