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Detection of Rice Leaf Folder in Paddy Fields Based on Unmanned Aerial Vehicle-Based Hyperspectral Images

文献类型： 外文期刊

作者： Feng, Shanshan 1 ; Jiang, Shun 1 ; Huang, Xuying 1 ; Zhang, Lei 1 ; Gan, Yangying 1 ; Wang, Laigang 3 ; Zhou, Canfang 1 ;

作者机构： 1.Guangdong Acad Agr Sci, Inst Agr Econ & Informat, Guangzhou 510640, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Urban Agr South China, Guangzhou 510640, Peoples R China

3.Henan Acad Agr Sci, Inst Agr Informat Technol, Zhengzhou 450002, Peoples R China

4.Minist Agr & Rural Affairs, Key Lab Huang Huai Hai Smart Agr Technol, Zhengzhou 450002, Peoples R China

关键词： rice pests; rice leaf folder; UAV; hyperspectral remote sensing; machine learning

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 11 期

页码：

收录情况： SCI

摘要： Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio680 (PSSR680), and Carotenoid Reflectance Index700 (CPI700). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model's feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation.

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