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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 The research focuses on addressing four main challenges: data scarcity, outdated network architectures, computational constraints of terminal devices, and resource and compatibility issues.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.
Liu et al. (Sat,) studied this question.