Affiliation:
1. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
2. School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
3. Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China
Abstract
It is of great practical significance to quickly, accurately, and effectively identify the effects of rice diseases on rice yield. This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the benchmark model and uses the channel attention mechanism squeeze-and-excitation to strengthen the favorable features, while suppressing the unfavorable features. Then, depth wise separable convolutions are introduced to replace some standard convolutions in the dense network to improve the parameter utilization and training speed. Using the AdaBound algorithm, combined with the adaptive optimization method, the parameter adjustment time reduces. In the experiments on five kinds of rice disease datasets, the average classification accuracy of the method in this paper is 99.4%, which is 13.8 percentage points higher than the original model. At the same time, it is compared with other existing recognition methods, such as ResNet, VGG, and Vision Transformer. The recognition accuracy of this method is higher, realizes the effective classification of rice disease images, and provides a new method for the development of crop disease identification technology and smart agriculture.
Funder
National Natural Science Foundation of China
Talents Scheme in Zhejiang Province
General scientific research project of Zhejiang Provincial Department of Education
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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