dataset

Abstract

 Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field-settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods are not only heavy workload, low efficiency, but also haphazard, unable to achieve real-time monitoring, which seriously limited the development of modern agriculture. Therefore, using target detection algorithm to identify rice diseases is an important research direction in the agricultural field. In this paper, the pictures of rice diseases taken in Jinzhai County, Lu 'an City, Anhui Province were taken as the research object, including rice leaf blast, bacterial blight and flax leaf spot, with a total of 7220 pictures. We propose a rice disease identification method based on the improved YOLOV5s, which reduces the computation of the backbone network, reduces the weight file of the model to 3.2MB, which is about 1/4 of the original model, and accelerates the prediction speed by 3 times. Compared with other mainstream methods, our method achieves the best performance with low computational cost. It solves the problem of slow recognition speed due to the large weight file and calculation amount of model when the model is deployed in mobile terminal </p