Wheat Yellow Rust Severity Detection by Efficient DF-UNet and UAV Multispectral Imagery

Abstract

Crop disease seriously affects production because of its highly destructive property. Wheat under different levels of disease infection should be treated by various chemical strategies to enable a precision plant protection. Therefore, a fast and robust algorithm for wheat yellow rust disease severity determination is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust severity detection at leaf level. However, little reviews take field-scale rust severity detection into account by using UAV multispectral images and deep learning networks. As a result, by the means of UAV multispectral images, a real-time yellow rust detection algorithm named Efficient Dual Flow UNet (DF-UNet) to detect different levels of yellow rust is designed and proposed in this paper to meet practical requirements. First, pruning strategy is utilized to realize a lightweight structure. Second, the Sparse Channel Attention (SCA) Module is designed to increase the receptive field of the network and enhance the ability to distinguish each category. Third, by fusing SCA, a novel dual flow branch model with segmentation and ranking branch based on UNet is proposed to accomplish yellow rust severity determination at field scale. The comparative results show that the proposed method reduces more than half computation load and achieves the highest overall accuracy score among other state-of-the-art deep learning models. It is convinced that the proposed DF-UNet can pave the way for automated yellow rust severity detection at farmland scales in a robust way.