Identification of Corn Leaf Spot Disease Based on Improved Xception

When using the UAV platform to identify crop diseases, due to the high resolution of the captured images and the small proportion of the target disease spots, the existing detection methods need to process images in multiple steps, which is time-consuming, laborious and low robust in detection effect. In order to reduce the image processing steps and improve the detection accuracy, this paper takes the maize large spot disease in the image captured by the drone as the detection object. First, the image is reduced and cropped according to a certain ratio, and the public data set is reconstructed using two different resolution images. Then an improved Xception network is adopted to reduce the loss of lesion feature information and improve the ability of feature information fusion by adding dense connections, and an attention module is integrated to adjust the image channel and suppress invalid information. Finally, the training model completes the identification of corn leaf spot and performs performance evaluation. The experimental results show that the recognition accuracy rate of the network proposed in this paper reaches 95.23%, and the recognition time of a single image is reduced to 0.5476 seconds, and the proposed model can effectively identify corn lesions in images taken by drones.