Early detection of rubber tree powdery mildew using UAV-based hyperspectral imagery and deep learning

文献类型： 外文期刊

第一作者： Zeng, Tiwei

作者： Zeng, Tiwei;Li, Yuan;Fu, Wei;Wang, Juan;Zhang, Xirui;Zeng, Tiwei;Wang, Yong;Yang, Yuqi;Liang, Qifu;Li, Yuan;Zhang, Huiming;Fu, Wei;Wang, Juan;Zhang, Xirui;Fang, Jihua;Li, Yuan;Fang, Jihua;Li, Yuan

作者机构：

关键词： Powdery mildew of rubber tree; Unmanned aerial vehicle; Hyperspectral imaging; Deep learning; Different spatial resolutions; Feature selection

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 220 卷

页码：

收录情况： SCI

摘要： Rubber tree powdery mildew(PM) is one of the most critical leaf diseases of rubber trees. The epidemic of this disease can seriously affect natural rubber yields and necessitates timely monitoring, especially in the early stages. In recent years, unmanned aerial vehicle(UAV) hyperspectral imaging technology has been widely used in the field of crop disease identification. Therefore, this paper proposes a rubber tree PM detection method based on UAV low -altitude remote sensing and hyperspectral imaging technology. Firstly, spectral reflectance wavelengths, vegetation indices(VIs), and texture features(TFs) were extracted from the region of interest of the UAV hyperspectral image. Then, random frog(RFrog) was performed to select the optimal wavelengths(OWs), Pearson correlation coefficient(PCC) and sequence backward selection(SBS) algorithm to select the effective VIs and TFs. Secondly, the multi -scale selective attention convolutional neural network(MSA-CNN) model was constructed to detect PM based on OWs, VIs, TFs and their combinations. Moreover, the original images with spatial resolution of 10 cm were resampled to different spatial resolutions(20 cm , 40 cm , 60 cm , and 80 cm ) to evaluate the effect of spatial resolution in PM monitoring. The results show that the proposed MSA-CNN model could sufficiently learn important features at different scales and obtain the best results in the full -wavelength dataset(OA = 93.79 %, Kappa = 92.16 %). Meanwhile, the models constructed using combining features(OWs + TFs, VIs + TFs, OWs + VIs + TFs) perform better than single features(OWs, VIs, TFs), and the highest performance was obtained for the OWs + VIs + TFs-based model(OA = 98.44 %, Kappa = 98.04 %). The optimal spatial resolution for PM monitoring was 10 cm . In addition, combining features could improve the classification accuracy of the early stages of PM. The results of the study provide a reference for accurate PM monitoring using UAV hyperspectral images.

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