摘要
Abstract
With the continuous development and maturity of deep learning technology in image recognition task,its algorithm is gradually applied to the recognition of agricultural pests and diseases,and achieves good results.However,the recognition of agri-cultural disease images is more challenging than that of agricultural diseases and pests.Therefore,taking RESNET,botnet and cot-net as the baseline,firstly,the structure of the network model is improved by introducing the channel attention mechanism,MHSA module and contextual transformer networks module,so that the improved network model can better extract the global and local fea-ture information of the image,and improve the feature expression ability of the network model.Secondly,the orthogonal projection loss function is combined with the traditional cross entropy loss function to reduce the interference of adverse factors such as label noise in the training process of image classification network model on agricultural disease data set,so as to optimize the training pro-cess and results of agricultural disease recognition.Finally,through several groups of experiments,it is proved that the improve-ment of the baseline model and the optimization of the training process can effectively improve the accuracy and robustness of the classification of the model on the agricultural disease data set,and the amount of parameters and calculation of the network model is reduced,so that the improved and optimized agricultural disease identification model can be more suitable for the actual work of crop disease identification,help the treatment of crop diseases,and empower the development of intelligent agriculture.关键词
农业病害识别/深度学习/ResNet/CotNet/多头注意力Key words
identification of agricultural diseases/deep learning/ResNet/CotNet/multiple attention分类
信息技术与安全科学
