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Review

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

1
Department of Food & Biological Engineering, Jiangsu University, Zhenjiang 212013, China
2
School of Electrical & Information Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(19), 2077; https://doi.org/10.3390/agriculture15192077
Submission received: 3 September 2025 / Revised: 28 September 2025 / Accepted: 1 October 2025 / Published: 3 October 2025

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 artificial intelligence; deep learning; pest and disease detection; pest and disease control; precision agriculture

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MDPI and ACS Style

Wu, Y.; Chen, L.; Yang, N.; Sun, Z. Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture 2025, 15, 2077. https://doi.org/10.3390/agriculture15192077

AMA Style

Wu Y, Chen L, Yang N, Sun Z. Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture. 2025; 15(19):2077. https://doi.org/10.3390/agriculture15192077

Chicago/Turabian Style

Wu, Yu, Li Chen, Ning Yang, and Zongbao Sun. 2025. "Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control" Agriculture 15, no. 19: 2077. https://doi.org/10.3390/agriculture15192077

APA Style

Wu, Y., Chen, L., Yang, N., & Sun, Z. (2025). Research Progress of Deep Learning-Based Artificial Intelligence Technology in Pest and Disease Detection and Control. Agriculture, 15(19), 2077. https://doi.org/10.3390/agriculture15192077

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