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Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery

Zhang, Tianxiang and Xu, Zhiyong and Su, Jinya and Yang, Zhifang and Liu, Cunjia and Chen, Wen-Hua and Li, Jiangyun (2021) 'Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery.' Remote Sensing, 13 (19). p. 3892. ISSN 2072-4292

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Abstract

Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm 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 disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales.

Item Type: Article
Uncontrolled Keywords: deep learning; Ir-UNet; crop disease detection; multispectral imagery; unmanned aerial vehicle (UAV)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 06 Oct 2021 11:46
Last Modified: 15 Jan 2022 01:38
URI: http://repository.essex.ac.uk/id/eprint/31167

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