Rice disease identification model based on multi-dimensional attention mechanism
Rapid and accurate identification of rice diseases is a prerequisite for controlling rice diseases and is one of the effective ways to improve rice yield and quality.To improve the identification accuracy of rice diseases,a network model of multi-dimensional attention mechanism for rice disease identification named Inter_3DRiceNet was proposed in this study to extract rice disease feature information through three different dimensions(channel dimension,height dimension and width dimension).The channel dimension mainly constructed a three-dimensional attention mechanism based on chan-nel relationship,and finally obtained three-dimensional attention feature information based on channel relationship by estab-lishing a one-dimensional attention mechanism of inter-channel relationship combined with two-dimensional spatial relationship.The height dimension established a three-di-mensional attention mechanism based on the height dimen-sion relationship,while the width dimension established a tridimensional attention mechanism based on the width di-mension relationship.The attention information of the a-bove three different dimensions was simply summed and then averaged as the final disease extraction features.Thus,be-sides more abundant features of the input images could be obtained,stereoscopic spatial relations of different dimensions could also be obtained.The experimental results showed that,the Inter_3DRiceNet network model proposed in the study got the highest accuracy of 98.32%in the test sets of the six self-constructed rice disease datasets in real natural environment,which was higher than the classical network models such as ResNet34,ResNet50,MobileNetV2,DenseNet,EfficientNet_B0,and channel attention mechanism models SENet and GCT.The research method improved the recognition accuracy of rice diseases effectively and obtained better classification accuracy than the classical network model and the channel atten-tion model,which can help improve the performance of common rice diseases recognition in natural environment.
万方数据
维普
