外文摘要：Nowadays, half of the global population depends on crop as their staple food, and crop yield is related to the food security of all mankind. crop pests and diseases are important factors affecting crop yield, and it is important to effectively prevent and efficiently detect crop pests and diseases. crop pests and diseases are diverse, previously mainly relying on manual experience to distinguish pests and disease types. With the extremely rapid development of deep learning technology, it is possible to recognise crop pests and diseases by technical methods. The diversity of pests and diseases makes higher requirements on the generalisation ability of the recognition model. In this paper, FRseNet is constructed based on ResNet-50 by introducing the SENet concept, and the experiments on the self-constructed crop pest and disease dataset show that it is capable of the task of recognising 15 kinds of diseases and 21 medium insect pests, and the performance is competitive.
外文关键词：disease recognition;Crop pests and diseases;Convolutional neural network(CNN)
作者：Xu, Wenqing;Li, Weikai
作者单位：Northeast Agr Univ
期刊名称：CROP PROTECTION
期刊影响因子：0.0
出版年份：2024
出版刊次：176
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台