Diagnosis and application of rice diseases based on deep learning.

Abstract

Background Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some current studies are based on images with simple backgrounds, while realistic scene settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods not only have heavy workload, low efficiency, but are also haphazard, unable to achieve real-time monitoring, which seriously limits the development of modern agriculture. Therefore, using target detection algorithm to identify rice diseases is an important research direction in the agricultural field. Methods In this article a total of 7,220 pictures of rice diseases taken in Jinzhai County, Lu'an City, Anhui Province were chosen as the research object, including rice leaf blast, bacterial blight and flax leaf spot. 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 three times. Results Compared with other mainstream methods, our method achieves better 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.

Plant phenotyping relevance

イネ葉の病害を画像から識別する改良YOLOv5s手法を開発し、精度・計算量・速度を評価しており、植物病害状態の画像ベース表現型計測が中心である。

Code and data availability

The authors publicly deposited the rice disease image dataset (7,220 images of rice leaf blast, bacterial blight, and flax leaf spot) on figshare, with an explicit data availability statement and DOI. Code is only available via supplemental files without an allowed public URL, so it is not listed as a separate asset.