基于无人机遥感的茶园多胁迫分层监测方法研究

打开文本图片集

中图分类号：S571.1；S127；S435.711

文献标志码：A

文章编号：1000-369X（2026）02-0292-19

Abstract： Tea[Camelia sinensis （L.）O.Kuntze] is an important economic crop in China.Its productionprocess 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 （Coletotrichum camellae），and proposed a stepwise multi-stress monitoring method based onunmanned 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 coeficient of variation （CV） of the plot's spectrum and linear discriminant analysis （LDA）. This model sucessfully categorized plots into entirelyhealthyplot （EHTP），entirelystressd plot （ESTP），and partiallystressed plot （PSTP），achievinganoverall accuracy of 94.7% . Based on this classification， a differentiated strategy was applied： UAV five-point sampling was used for stress asessment and health validation in ESTPand 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 clasification was then caried 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 eficiency of multi-stressidentification in tea garden，providing technical support for smart tea garden management and ofering a methodological reference for other economic crops.

Keywords： UAV remote sensing， multi-stress monitoring，tea garden，tea row extraction， multi-step strategy

茶树[Camellia sinensis （L.） O.Kuntze]是我国重要的经济作物，其田间生长经常受到病、虫、热害等胁迫，导致茶叶产量和品质下降[1]。（剩余26264字）