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Monitoring the Severity of Rubber Tree Infected with Powdery Mildew Based on UAV Multispectral Remote Sensing

文献类型： 外文期刊

作者： Zeng, Tiwei 1 ; Zhang, Huiming 2 ; Li, Yuan 1 ; Yin, Chenghai 1 ; Liang, Qifu 2 ; Fang, Jihua 3 ; Fu, Wei 1 ; Wang, Juan 2 ; Zhang, Xirui 1 ;

作者机构： 1.Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Peoples R China

2.Hainan Univ, Mech & Elect Engn Coll, Haikou 570228, Peoples R China

3.Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Haikou 571000, Peoples R China

4.Key Lab Pract Res Trop Crops Informat Technol Hain, Haikou 571000, Peoples R China

关键词： UAV; multispectral images; powdery mildew diseases; rubber tree; multi-feature fusion; PCC-SBS feature selection; machine learning

期刊名称：FORESTS （ 影响因子：2.9； 五年影响因子：3.0 ）

ISSN：

年卷期： 2023 年 14 卷 4 期

页码：

收录情况： SCI

摘要： Rubber tree powdery mildew (PM) is one of the most devastating leaf diseases in rubber forest plantations. To prevent and control PM, timely and accurate detection is essential. In recent years, unmanned Aerial Vehicle (UAV) remote sensing technology has been widely used in the field of agriculture and forestry, but it has not been widely used to detect forest diseases. In this study, we propose a method to detect the severity of PM based on UAV low-altitude remote sensing and multispectral imaging technology. The method uses UAVs to collect multispectral images of rubber forest canopies that are naturally infected, and then extracts 19 spectral features (five spectral bands + 14 vegetation indices), eight texture features, and 10 color features. Meanwhile, Pearson correlation analysis and sequential backward selection (SBS) algorithm were used to eliminate redundant features and discover sensitive feature combinations. The feature combinations include spectral, texture, and color features and their combinations. The combinations of these features were used as inputs to the RF, BPNN, and SVM algorithms to construct PM severity models and identify different PM stages (Asymptomatic, Healthy, Early, Middle and Serious). The results showed that the SVM model with fused spectral, texture, and color features had the best performance (OA = 95.88%, Kappa = 0.94), as well as the highest recognition rate of 93.2% for PM in early stages.

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