A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.

Abstract

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

Plant phenotyping relevance

UAV画像から小麦の黄さび病状態を意味的セグメンテーションで推定する手法を開発・評価しており、植物病害状態の取得が研究の中心である。

Code and data availability

The supplied blocks describe UAV RGB image acquisition, annotation, and PSPNet/SVM/RF/BPNN/FCN/U-Net experiments, but contain no data availability statement, no public dataset deposit, and no author code/model release. The UAV images, labeled dataset (5580 samples), and trained model are not stated as publicly shared,

No evidence-backed public reproduction asset is currently recorded.