深度学习在作物病害识别方面的研究进展OA

In recent years,with the rapid development of computer vision technology,intelligent disease recognition systems based on digital image processing have demonstrated remarkable application potential in early diagnosis and precise control of crop diseases due to their efficiency and accuracy.This paper systematically reviews research progress in deep learning techniques for crop disease recognition.Through a comparative analysis with traditional machine learning methods,it highlights the technical advantages and application limitations of deep learning algorithms in disease feature extraction and classification.Furthermore,the paper analyzes the comprehensive technical workflow of deep learning in crop disease recognition and enumerates application cases utilizing mainstream network architectures.On this basis,the paper discusses key technical challenges faced by deep learning applications for crop disease recognition in complex field environments and provides perspectives on future research directions.The aim is to provide theoretical foundation and technical support for promoting the practical application of intelligent early warning and precise recognition technologies for crop diseases in modern agricultural production systems.