茶叶科学 >

2026 , Vol. 46 >Issue 2: 292 - 310

DOI: https://doi.org/10.13305/j.cnki.jts.2026.02.010

研究报告

基于无人机遥感的茶园多胁迫分层监测方法研究

  • 余盈潭 ,
  • 袁琳 ,
  • 聂臣巍 ,
  • 金子晶 ,
  • 陈冬梅 ,
  • 李征珍 ,
  • 李鑫
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  • 1.浙江理工大学信息科学与工程学院,浙江 杭州 310018;
    2.浙江水利水电学院计算机科学与技术学院,浙江 杭州 310018;
    3.浙江省农业技术推广中心,浙江 杭州 310020;
    4.杭州电子科技大学人工智能学院,浙江 杭州 310018;
    5.中国农业科学院茶叶研究所,浙江 杭州 310008
余盈潭,女,硕士研究生,主要从事无人机遥感病虫害监测方面的研究。

收稿日期: 2025-09-26

  修回日期: 2025-12-09

  网络出版日期: 2026-04-22

基金资助

国家自然科学基金（42371385）; 浙江省自然科学基金（LTGN23D010002、ZCLZ24F0201）; 杭州市自然科学基金（2024SZRYBD010001）

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A Multi-step Unmanned Aerial Vehicle Remote Sensing Approach for Monitoring Stresses in Tea Garden

  • YU Yingtan ,
  • YUAN Lin ,
  • NIE Chenwei ,
  • JIN Zijing ,
  • CHEN Dongmei ,
  • LI Zhengzhen ,
  • LI Xin
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  • 1. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China;
    2. School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China;
    3. Zhejiang Agricultural Technology Extension Center, Hangzhou 310020, China;
    4. School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China;
    5. Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou 310008, China

Received date: 2025-09-26

  Revised date: 2025-12-09

  Online published: 2026-04-22

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摘要

茶树[Camellia sinensis (L.) O. Kuntze]是我国重要的经济作物,其生产过程易受到病虫害等胁迫的影响,进而造成产量和品质下降。因此,对茶园胁迫精准监测是实现茶园精细化管理的关键环节。以茶尺蠖（Ectropis obliqua）、热害和炭疽病（Colletotrichum camelliae）3种典型胁迫为对象,提出了一种基于无人机遥感的茶园多胁迫分层监测方法。研究首先针对茶园垄行结构的特点,结合RedEdge波段下的决策树与边缘检测算法（DT-ED）,实现了茶行的高精度提取;其次,考虑到胁迫在茶园中的空间分布差异,基于地块光谱变异系数（CV）和线性判别分析（LDA）,进一步构建了地块类型判别模型（整体健康、整体异常、局部异常）,总体精度达到94.7%。在此基础上,针对整体异常和健康地块,采用无人机五点抽样法进行胁迫评估与健康状态验证;针对局部异常地块,则通过“异常区识别-胁迫类型判别”的两步策略展开监测,其中异常区利用“两阶段聚类策略”划分。分别采用支持向量机（SVM）、K近邻（KNN）和多层感知机（MLP）等算法建模对胁迫类型进行判别,结果显示,MLP模型识别准确率最高（92.3%）。研究提出的分层监测方法能够有效提升茶园多胁迫识别的准确性和效率,为茶园智慧化管理提供了技术支撑,也为其他经济作物胁迫监测提供参考。

关键词： 茶行提取; 茶园; 多胁迫监测; 分层策略; 无人机遥感

本文引用格式

余盈潭 , 袁琳 , 聂臣巍 , 金子晶 , 陈冬梅 , 李征珍 , 李鑫 . 基于无人机遥感的茶园多胁迫分层监测方法研究[J]. 茶叶科学, 2026 , 46(2) : 292 -310 . DOI: 10.13305/j.cnki.jts.2026.02.010

Abstract

Tea [Camellia sinensis (L.) O. Kuntze] is an important economic crop in China. Its production process is highly susceptible to stresses such as pests and diseases, which subsequently lead to a reduction in yield and quality. Accurate monitoring of stress conditions in tea garden is therefore essential for precision and smart management. This study focused on three typical stresses: tea geometrid (Ectropis obliqua), heat stress and anthracnose (Colletotrichum camelliae), and proposed a stepwise multi-stress monitoring method based on unmanned aerial vehicle (UAV) remote sensing. The research first focused on the characteristics of tea garden ridge-and-furrow structures. By combining a decision tree and edge detection (DT-ED) algorithm, which utilizes the RedEdge band, high-precision extraction of tea rows was achieved. Subsequently, considering the spatial distribution differences of stress within tea garden plots, a plot type discrimination model was constructed based on the coefficient of variation (CV) of the plot's spectrum and linear discriminant analysis (LDA). This model successfully categorized plots into entirely healthy plot (EHTP), entirely stressed plot (ESTP), and partially stressed plot (PSTP), achieving an overall accuracy of 94.7%. Based on this classification, a differentiated strategy was applied: UAV five-point sampling was used for stress assessment and health validation in ESTP and EHTP plots, while a two-step approach of “abnormal zone detection-stress type identification” was applied to PSTP plots. The abnormal zones were delineated using two-stage clustering strategy. Stress type classification was then carried out using algorithms such as support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the MLP achieved the best performance, with an overall accuracy of 92.3%. The findings demonstrate that the proposed multi-step monitoring method can effectively improve the accuracy and efficiency of multi-stress identification in tea garden, providing technical support for smart tea garden management and offering a methodological reference for other economic crops.

Key words： multi-step strategy; multi-stress monitoring; tea garden; tea row extraction; UAV remote sensing

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