Discrimination of tea varieties and bud sprouting phenology using UAV-based RGB and multispectral images

文献类型： 外文期刊

第一作者： Sun, Litao

作者： Sun, Litao;Shen, Jiazhi;Li, Xiaojiang;Ding, Zhaotang;Sun, Litao;Sun, Litao;Xu, Yang;Ding, Zhaotang;Mao, Yilin;Fan, Kai;Qian, Wenjun;Wang, Yu;Bi, Caihong;Wang, Hui;Xu, Jingbiao

作者机构：

关键词： Camellia sinensis; variety classification; phenology; neural networks; machine learning

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：2.6； 五年影响因子：2.9 ）

ISSN： 0143-1161

年卷期： 2025 年 46 卷 16 期

页码：

收录情况： SCI

摘要： Tea plant germplasm resources are abundant in China. Effectively identifying tea varieties and evaluating the tea bud sprouting period (BSP) are critical for tea plant breeding and tea plantation management. This study focused on key tea varieties cultivated in the tea-growing regions of Shandong Province, China. UAV-based multispectral and RGB images of seven tea varieties were collected before and during the budding period in spring. Five machine learning classification algorithms, LSTM, BP, PSO-BP, GA-BP and SVM were employed to classify tea varieties and BSP. Results indicated significant spectral differences among tea varieties across six spectral bands. Spectral differences among varieties collected during budding became more pronounced than that pre-budding period. Machine learning techniques effectively distinguished different tea varieties and BSP. Models of LSTM, BP and PSO-BP established by data collected during budding enhanced the classification accuracy of tea varieties by 0.45 similar to 2.11% than that before budding. The integration of indices and texture features from both sampling periods further improved classification accuracy of tea varieties. BP model achieved the highest variety and BSP classification accuracy with a test set accuracy of 93.65% and 92.86%, respectively, followed by LSTM with the accuracy of 93.65% and 90.48%, respectively. Considering computational speed and accuracy, the BP classification model was well-suited for real-time classification needs in various application scenarios. This study provides technical support for large-scale tea variety classification and budding phenology monitoring. It also serves as a valuable reference for the rapid screening, identifying, and improving tea plant superior germplasm, thereby enhancing breeding efficiency.

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