关键词: 无人机, 茶树, 种植信息提取, 植被指数, 纹理特征

Abstract: As a crucial economic crop, the rapid measurement of tea planting areas holds significant value for estimating tea yield, optimizing tea garden management strategies. The study employs UAV to obtain multispectral remote sensing images of tea plants. It selects vegetation indices, texture features, and their combined features as multivariate analysis indicators. By using the RF model and correlation analysis, the study evaluates the importance of these indicators and performs correlation tests. Subsequently, three supervised classification algorithms, namely MLC, SVM, and RF are utilized to precisely identify the distribution of tea plants plantations in the study area. The results reveal that: ①When using the SVM and RF algorithms, the incorporation of texture features or multi-feature combinations notably enhances classification accuracy compared with using a single vegetation index feature. ②The optimal classification feature combination for the study area is identified as the visible vegetation indices combined with texture features, achieving a total classification accuracy of 95.5% and a Kappa coefficient of 0.917 with the SVM algorithm. ③The use of full-feature datasets and feature dimensionality reduction datasets does not significantly enhance classification accuracy; however, the latter demonstrates the highest stability in classification results and achieves an accuracy that is second only to the optimal classification feature datasets. In summary, by utilizing the SVM algorithm,the fusion of visible vegetation indices and texture features can effectively differentiate tea plants from other land features,achieve high-precision extraction of tea planting information,and provide valuable reference and practical guidance for accurately extracting crop planting information.

Key words: UAV, tea plants, plantation information extraction, vegetation indices, texture features