Forests (2023) 14 (4 - 717)

Tiwei Zeng, Huiming Zhang, Yuan Li, Chenghai Yin, Qifu Liang, Jihua Fang, Wei Fu, Juan Wang and Xirui Zhang (2023)
Monitoring the severity of rubber tree infected with powdery mildew based on UAV multispectral remote sensing
Forests 14 (4 - 717)
Abstract: 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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Research topic(s) for pests/diseases/weeds:
surveys/sampling/distribution

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Pest and/or beneficial records:

Beneficial	Pest/Disease/Weed	Crop/Product	Country	Quarant.
	Oidium heveae	Rubber (Hevea brasiliensis)	China (south)