Manchester Metropolitan University's Research Repository

A deep learning-based approach for automated yellow rust disease detection from high resolution hyperspectral UAV images

Zhang, Xin, Han, Liangxiu ORCID logoORCID: https://orcid.org/0000-0003-2491-7473, Dong, Yingying, Shi, Yue, Huang, Wenjiang, Han, lianghao ORCID logoORCID: https://orcid.org/0000-0003-2491-7473, González-Moreno, Pablo, Ma, Huiqin, Ye, Huichun and Sobeih, Tam (2019) A deep learning-based approach for automated yellow rust disease detection from high resolution hyperspectral UAV images. Remote Sensing, 11 (13). pp. 1554-1570. ISSN 2072-4292

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Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in Unmanned Aerial Vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

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