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

Tianxiang Zhang,Zhifang Yang,Jinya Su,Wen-Hua Chen,Cunjia Liu,Zhiyong Xu,Jiangyun Li

doi:10.3390/rs13193892

Tianxiang Zhang, Zhifang Yang + Show 5 more

Open Access

https://doi.org/10.3390/rs13193892

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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.

Highlights

Crop diseases are widely considered as one of the most pressing challenges for food crops, seriously threatening crop quality and safety because a safe food supply begins with protecting crops from diseases and toxins
This paper investigates the challenging problem of wheat yellow rust detection by integrating unmanned aerial vehicle (UAV) multispectral imaging and deep learning method
irregular encoder module (IEM) and irregular decoder module (IDM) are fused into the basic UNet network to cope with irregular and blurred boundary problems of remote sensing dataset so that a more reliable and accurate Ir-UNet network is proposed for yellow rust disease detection automatically

Summary

Introduction

Crop diseases are widely considered as one of the most pressing challenges for food crops, seriously threatening crop quality and safety because a safe food supply begins with protecting crops from diseases and toxins. The conventional crop disease control method is mainly calendar-based pesticide application regardless of the current disease development and risks. This method leads to a high cost and generates adverse environmental impact. It is paramount to use a decision-based disease control method to improve the detection accuracy for crop disease control and management. Some steps need to be accomplished for pre-processing. These steps can be performed offline to generate calibrated and georeferenced reflectance data for each spectral band. The problem of detecting and quantifying wheat yellow rust disease can be treated as a semantic segmentation task. The rust regions (polygons) are first labelled on the false-colour

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