Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques—including image classification, object detection, semantic segmentation, and change detection—to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.

Based on the results of experimental investigations performed on the polluted and iced IEC standard suspension insulator strings of 3~21 units, the ac flashover performance and discharge process are discussed in this paper. It is put forward that the icing stress product (ISP), defined as the product of the ice-layer conductivity and the ice layer weight per centimeter of dry arc distance can be taken as a characteristic parameter to indicate the effect of ice accretions and contaminants on the flashover voltage. The flashover stress of an ice-covered insulator string can be expressed as a power function of ISP and has a linear relationship with the length of the insulator string. The partial arc of an iced insulator string is initiated by the breakdown of the air gaps between the tips of icicles and iced insulator surface and the persistence is much longer than that of a polluted insulator string