Crop pests and diseases pose a significant threat to global agricultural production,often lead-ing to substantial economic losses.The application of machine vision and machine learning methods for crop pest and disease monitoring not only enhances detection efficiency,but also facilitates timely preventive meas-ures to reduce losses.Convolutional neural network(CNN),as a prominent deep learning technique,has been widely used in computer vision for image and object recognition,and also has achieved promising a-chieves in crop disease and pest detection.This paper outlined the key aspects and development history of CNN-based techniques for crop disease and pest detection,reviewed the major research directions and ad-vancements in this area,summarized the main issues in current research,and proposed corresponding solu-tions.The aim of this paper was to provide a theoretical basis for CNN application in agriculture and to offer technical supports for the intelligent management of agricultural production.
关键词
农作物病虫害检测/卷积神经网络/深度学习/计算机视觉
Key words
Crop disease and pest detection/Convolutional neural network/Deep learning/Computer vision
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