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Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

文献类型： 外文期刊

作者： Qiao, Dan 1 ; Yang, Juntao 1 ; Bai, Bo 2 ; Li, Guowei 2 ; Wang, Jianguo 2 ; Li, Zhenhai 1 ; Liu, Jincheng 3 ; Liu, Jiayin 1 ;

作者机构： 1.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China

2.Shandong Acad Agr Sci, Inst Crop Germplasm Resources, Jinan 250100, Peoples R China

3.Northwest A&F Univ, Coll Nat Resources & Environm, Xianyang 712100, Peoples R China

关键词： leaf area index; vegetation indices; texture characteristics; peanut; UAV remote sensing

期刊名称：REMOTE SENSING （ 影响因子：4.2； 五年影响因子：4.9 ）

ISSN：

年卷期： 2024 年 16 卷 12 期

页码：

收录情况： SCI

摘要： The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson's product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

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