Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control

Yu Wu, Li Chen, Ning Yang and Zongbao Sun ()
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Yu Wu: Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Li Chen: Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Ning Yang: School of Electrical & Information Engineering, Jiangsu University, Zhenjiang 212013, China
Zongbao Sun: Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2025, vol. 15, issue 19, 1-36

Abstract: With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development.

Keywords: artificial intelligence; deep learning; pest and disease detection; pest and disease control; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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