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Ir-UNet: Irregular segmentation U-shape network for wheat yellow rust detection by UAV multispectral imagery

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posted on 30.09.2021, 09:11 authored by Tianxiang Zhang, Zhiyong Xu, Jinya Su, Zhifang Yang, Cunjia LiuCunjia Liu, Wen-Hua ChenWen-Hua Chen, Jiangyun Li
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.

Funding

Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002)

Beijing Key Discipline Development Program (No. XK100080537)

Space-enabled Crop disEase maNagement sErvice via Crop sprAying Drones (SCENE-CAD)

Science and Technology Facilities Council

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History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Remote Sensing

Volume

13

Issue

19

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

23/09/2021

Publication date

2021-09-28

Copyright date

2021

eISSN

2072-4292

Language

en

Depositor

Prof Wen-Hua Chen. Deposit date: 29 September 2021

Article number

3892