关键词: 深度学习, 植物病害, 特征提取, 密集连接, 图像识别

Abstract:  With the close combination of deep learning technology and agriculture, more researches have applied deep learning technology to recognize agricultural diseases. Agriculture with deep learning improves the yield and quality of agricultural products. This paper proposes a new Xceptionbased method for the identification of plant diseases. Considering the image of plant diseases will be disturbed by uncertain environmental factors and reduce the image information on the basis of Xception, a new Channel Expand Module (CEM) module adopting the combination of Multiscale Depthwise Separable Convolution with channel distribution weight and group convolution was proposed. This enhanced the feature extraction efficiency of space and channel as well as the new strategy of channel expansion which included channel expansionmaintainreexpansioncompression to further optimize the channel feature extraction in the network. The novel dense connection was proposed to improve feature reuse among feature maps of the same size. The experimental data set consisted of 50 types of images of 10 different plants, including 10 healthy plants and 27 diseases,  which 13 diseases were classified into two levels. The algorithm obtained 91.9% accuracy, 88.7% precision, 8245% recall, and 85.33% F1Score. Compared with Xception architecture, our network had a smaller model complexity with FLOPs being 29.33 M, which was 66.4% of Xception while smaller parameter amount with parameter amount being 14.05 M, which was 66.9% of Xception. Therefore, XceptionCEMs can effectively identify diseases, which is conducive to the development of intelligent agriculture.

Key words: deep learning, plant disease, feature extraction, dense connection, image recognition