Semantic Segmentation of Wheat Stripe Rust Images Using Deep Learning

Abstract

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset.