基于无人机遥感的茶园多胁迫分层监测方法研究

doi:10.13305/j.cnki.jts.2026.02.010

Abstract

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

CLC Number:

YU Yingtan, YUAN Lin, NIE Chenwei, JIN Zijing, CHEN Dongmei, LI Zhengzhen, LI Xin. A Multi-step Unmanned Aerial Vehicle Remote Sensing Approach for Monitoring Stresses in Tea Garden[J]. Journal of Tea Science, 2026, 46(2): 292-310.

References

[1] 杨芳, 江冰冰, 雷金梅, 等. 茶树叶斑病病原菌的分离与鉴定[J]. 茶叶科学, 2025, 45(2): 266-272.
Yang F, Jiang B B, Lei J M, et al.Isolation and identification of the pathogen causing leaf spot in tea plants[J]. Journal of Tea Science, 2025, 45(2): 266-272.
[2] Mao Y L, Li H, Wang Y, et al.Rapid monitoring of tea plants under cold stress based on UAV multi-sensor data[J]. Computers and Electronics in Agriculture, 2023, 213: 108176. doi: 10.1016/j.compag.2023.108176.
[3] Zhang J C, Huang Y B, Pu R L, et al.Monitoring plant diseases and pests through remote sensing technology: a review[J]. Computers and Electronics in Agriculture, 2019, 165: 104943. doi: 10.1016/j.compag.2019.104943.
[4] Lu Y Z, Hu Y G, Snyder R L, et al.Tea leaf’s microstructure and ultrastructure response to low temperature in indicating critical damage temperature[J]. Information Processing in Agriculture, 2019, 6(2): 247-254.
[5] Gao J M, Ding M L, Sun Q Y, et al.Classification of southern corn rust severity based on leaf-level hyperspectral data collected under solar illumination[J]. Remote Sensing, 2022, 14(11): 2551. doi: 10.3390/rs14112551.
[6] Su J Y, Liu C J, Coombes M, et al.Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery[J]. Computers and Electronics in Agriculture, 2018, 155: 157-166. doi: 10.1016/j.compag.2018.10.017.
[7] Wang T Y, Thomasson J A, Yang C H, et al.Automatic classification of cotton root rot disease based on UAV remote sensing[J]. Remote Sensing, 2020, 12(8): 1310. doi: 10.3390/rs12081310.
[8] Chakhvashvili E, Machwitz M, Antala M, et al.Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies[J]. Precision Agriculture, 2024, 25: 2614-2642. doi: 10.1007/s11119-024-10168-3.
[9] Wikantika K, Ghazali M F, Dwivany F M, et al.A study on the distribution pattern of banana blood disease (BBD) and Fusarium Wilt using multispectral aerial photos and a handheld spectrometer in Subang, Indonesia[J]. Diversity, 2023, 15(10): 1046. doi: 10.3390/d15101046.
[10] Yuan L, Zhang J C, Deng Q, et al.Differentiation of wheat diseases and pests based on hyperspectral imaging technology with a few specific bands[J]. Phyton, 2023, 92(2): 611-628.
[11] Chen Y W, Guo T F, Shi X Y, et al.Development of a nitrogen requirement map generation method for potato based on UAV visible light image[J]. Computers and Electronics in Agriculture, 2025, 233: 110181. doi: 10.1016/j.compag.2025.110181.
[12] Sun H G, Song X Y, Guo W, et al.Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation[J]. Computers and Electronics in Agriculture, 2023, 215: 108438. doi: 10.1016/j.compag.2023.108438.
[13] Tian B Q, Yu H L, Zhang S L, et al.Inversion of cotton soil and plant analytical development based on unmanned aerial vehicle multispectral imagery and mixed pixel decomposition[J]. Agriculture, 2024, 14(9): 1452. doi: 10.3390/agriculture14091452.
[14] Zhang H S, Zhao J L, Huang L S, et al.Development of new indices and use of CARS-Ridge algorithm for wheat fusarium head blight detection using in-situ hyperspectral data[J]. Biosystems Engineering, 2024, 237: 13-25. doi: 10.1016/j.biosystemseng.2023.11.009.
[15] Guo W, Gong Z, Gao C F, et al.An accurate monitoring method of peanut southern blight using unmanned aerial vehicle remote sensing[J]. Precision Agriculture, 2024, 25(4): 1857-1876.
[16] Kan Z Y, Chen B, Yu W W, et al.Risk identification of mangroves facing Spartina alterniflora invasion using data-driven approaches with UAV and machine learning models[J]. Remote Sensing of Environment, 2025, 319: 114613. doi: 10.1016/j.rse.2025.114613.
[17] 郑国华. 炭疽病侵染对枇杷叶片H₂O₂含量和叶绿素荧光参数的影响[J]. 福建农业大学学报, 2001, 30(3): 353-356.
Zheng G H.Effect of anthracnose infection on the content of H₂O₂ and chlorophllous fluorescence in loquat leaves[J]. Fujian Journal of Agricultural Sciences, 2001, 30(3): 353-356.
[18] 韦朝领, 童鑫, 高香凤, 等. 茶树对茶尺蠖取食危害的补偿光合生理反应研究[J]. 安徽农业大学学报, 2007(3): 355-359.
Wei C L, Tong X, Gao X F, et al.Compensatory photosynthesis physiology of tea plant for herbivory by Ectropis oblique[J]. Journal of Anhui Agricultural University, 2007(3): 355-359.
[19] 杨菲, 李蓓蓓, 何辰宇. 高温干旱对茶树生长和品质影响机理的研究进展[J]. 江苏农业科学, 2017, 45(3): 10-13.
Yang F, Li B B, He C Y.Research progress on the mechanism of high temperature and drought stress effects on tea plant growth and quality[J]. Jiangsu Agricultural Sciences, 2017, 45(3): 10-13.
[20] Berger K, Machwitz M, Kycko M, et al.Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: a review[J]. Remote Sensing of Environment, 2022, 280: 113198. doi: 10.1016/j.rse.2022.113198.
[21] Rouse J W, Haas R H, Schell J A, et al.Monitoring vegetation systems in the great plains with ERTS[J]. NASA Special Publication, 1974, 351: 309-317.
[22] Gitelson A A, Viña A, Arkebauer T J, et al.Remote estimation of leaf area index and green leaf biomass in maize canopies[J]. Geophysical Research Letters, 2003, 30(5): 1248. doi: 10.1029/2002GL016450.
[23] Rondeaux G, Steven M, Baret F.Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55(2): 95-107.
[24] El-Shikha D M, Barnes E M, Clarke T R, et al. Remote sensing of cotton nitrogen status using the canopy chlorophyll content index (CCCI)[J]. Transactions of the ASABE, 2008, 51(1): 73-82.
[25] Patrick A, Pelham S, Culbreath A, et al.High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging[J]. IEEE Instrumentation & Measurement Magazine, 2017, 20(3): 4-12.
[26] Gitelson A A, Merzlyak M N, Chivkunova O B.Optical properties and nondestructive estimation of anthocyanin content in plant leaves[J]. Photochemistry and Photobiology, 2001, 74(1): 38-45.
[27] Roujean J L, Breon F M.Estimating par absorbed by vegetation from bidirectional reflectance measurements[J]. Remote Sensing of Environment, 1995, 51(3): 375-384.
[28] Jordan C F.Derivation of leaf-area index from quality of light on the forest floor[J]. Ecology, 1969, 50(4): 663-666.
[29] Gitelson A A, Kaufman Y J, Merzlyak M N.Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298.
[30] Qi J, Chehebouni A, Huete A R, et al.A modified soil adjusted vegetation index[J]. Remote Sensing of Environment, 1994, 48: 119-126.
[31] Chen J M.Evaluation of vegetation indices and a modified simple ratio for boreal applications[J]. Canadian Journal of Remote Sensing, 1996, 22(3): 229-242.
[32] Jiang Z, Huete A R, Didan K, et al.Development of a two-band enhanced vegetation index without a blue band[J]. Remote Sensing of Environment, 2008, 112(10): 3833-3845.
[33] Gitelson A A, Kaufman Y J, Stark R, et al.Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sensing of Environment, 2002, 80(1): 76-87.
[34] Gitelson A A.Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation[J]. Journal of Plant Physiology, 2004, 161(2): 165-173.
[35] Ortuani B, Mayer A, Bianchi D, et al.Effectiveness of management zones delineated from UAV and Sentinel-2 data for precision viticulture applications[J]. Remote Sensing, 2024, 16(4): 635. doi: 10.3390/rs16040635.
[36] Wu W, Zhong X C, Lei C K, et al.Sampling survey method of wheat ear number based on UAV images and density map regression algorithm[J]. Remote Sensing, 2023, 15(5): 1280. doi: 10.3390/rs15051280.
[37] Chandra S, Kumar S, Kumar Y.Performance analysis of metaheuristic algorithms for solving data clustering problems[J]. Procedia Computer Science, 2025, 259: 80-87. doi: 10.1016/j.procs.2025.03.309.
[38] Lemenkova P.Support vector machine algorithm for mapping land cover dynamics in Senegal, West Africa, using earth observation data[J]. Earth, 2024, 5(3): 420-462.
[39] Fan X S, Zhang Y, Peng Y, et al.LO-MLPRNN: a classification algorithm for multispectral remote sensing images by fusing selective convolution[J]. Sensors, 2025, 25(8): 2472. doi: 10.3390/s25082472.
[40] Abdullah Salem B T S, Abdullah M N, Mustapha F, et al. Vibration analysis using multi-layer perceptron neural networks for rotor imbalance detection in quadrotor UAV[J]. Drones, 2025, 9(2): 102. doi: 10.3390/drones9020102.
[41] Zhou Q Y, Zhang J C, Wei T W, et al.Prediction of chlorophyll relative content in tea plant canopy using optimize GRNN algorithm and RPA multispectral images[J]. Ciência e Agrotecnologia, 2024, 48: e016123. doi: 10.1590/1413-7054202448016123.
[42] Gitelson A A, Viña A, Ciganda V, et al.Remote estimation of canopy chlorophyll content in crops[J]. Geophysical Research Letters, 2005, 32(8): L08403. doi: 10.1029/2005GL022688.
[43] Li N W, Huo L N, Zhang X L.Using only the red-edge bands is sufficient to detect tree stress: a case study on the early detection of PWD using hyperspectral drone images[J]. Computers and Electronics in Agriculture, 2024, 217: 108665. doi: 10.1016/j.compag.2024.108665.
[44] Gitelson A A, Stark R, Grits U, et al.Vegetation and soil lines in visible spectral space: a concept and technique for remote estimation of vegetation fraction[J]. International Journal of Remote Sensing, 2002, 23(13): 2537-2562.
[45] Yang H B, Li F, Hu Y C, et al.Hyperspectral indices optimization algorithms for estimating canopy nitrogen concentration in potato (Solanum tuberosum L.)[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102416. doi: 10.1016/j.jag.2021.102416.
[46] Henebry G M, Viña A, Gitelson A A. The wide dynamic range vegetation index and its potential utility for gap analysis [J]. Gap Analysis Bulletin, 2004, 12: 50-56. https://digitalcommons.unl.edu/natrespapers/262.
[47] Jiang J, Ji H T, Zhou G Z, et al.Non-destructive monitoring of tea plant growth through UAV spectral imagery and meteorological data using machine learning and parameter optimization algorithms[J]. Computers and Electronics in Agriculture, 2025, 229: 109795. doi: 10.1016/j.compag.2024.109795.
[48] Xu X H, Yang J H, Zhang Y, et al.Ecological risk assessment of heavy metals in tea plantation soil around Tai Lake region in Suzhou, China[J]. Stress Biology, 2024, 4(1): 15. doi: 10.1007/s44154-024-00149-x.
[49] Yuan L, Yan P, Han W Y, et al.Detection of anthracnose in tea plants based on hyperspectral imaging[J]. Computers and Electronics in Agriculture, 2019, 167: 105039. doi: 10.1016/j.compag.2019.105039.
[50] 唐美君, 李红, 张欣欣, 等. 温度对灰茶尺蠖幼虫龄期数量的影响[J]. 茶叶科学, 2025, 45(1): 79-86.
Tang M J, Li H, Zhang X X, et al.Determination of the larval instar numbers of the Ectropis grisescens at different temperatures[J]. Journal of Tea Science, 2025, 45(1): 79-86.
[51] 娄伟平, 肖强, 孙科, 等. 浙江省茶树高温热害风险区划[J]. 茶叶科学, 2018, 38(5): 480-486.
Lou W P, Xiao Q, Sun K, et al.Heat stress risk regionalization of tea plant in Zhejiang Province[J]. Journal of Tea Science, 2018, 38(5): 480-486.
[52] Zheng C K, Sun L, Huang H G, et al.Determination of sample fire points by combined medium and high-resolution datasets and its implementation in Himawari-8 to detect fire: deep learning algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024: 3514777. doi: 10.1109/JSTARS.2024.3514777.
[53] Zhao M H, Wang D S, Yan Q, et al.UAV-multispectral based maize lodging stress assessment with machine and deep learning methods[J]. Agriculture, 2024, 15(1): 36. doi: 10.3390/agriculture15010036.
[54] Shi W Q, Li Y H, Zhang W, et al.Monitoring and zoning soybean maturity using UAV remote sensing[J]. Industrial Crops and Products, 2024, 222: 119470. doi: 10.1016/j.indcrop.2024.119470.
[55] Yuan L, Yu Q M, Zhang Y, et al.Monitoring Thosea sinensis Walker in tea plantations based on UAV multi-spectral image[J]. Phyton-International Journal of Experimental Botany, 2022, 92(3): 747-761.
[56] Manzoor, Dai Q, Mo Y X, et al. Habitat and density effects on tea quality: a microclimate and nutrients perspective[J]. Agriculture, Ecosystems & Environment, 2025, 394: 109866. doi: 10.1016/j.agee.2025.109866.
[57] Liang D X, Zhou Q Y, Ling C J, et al.Research progress on the application of hyperspectral imaging techniques in tea science[J]. Journal of Chemometrics, 2023, 37(6): e3481. doi: 10.1002/cem.3481.
[58] Ribes M, Russias G, Tregoat D, et al.Towards low-cost hyperspectral single-pixel imaging for plant phenotyping[J]. Sensors, 2020, 20(4): 1132. doi: 10.3390/s20041132.

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

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	ZHANG Jie, HU Qiang, HONG Qingyu, JIANG Xinfeng, WU Weizhen, XU Hao, ZHAN Jie, FU Jianyu, LI Xin, YAN Peng, YU Jizhong, HUANG Weihong. Effects of Intercropping on the Ecological Environment and Economic Benefits of Tea Gardens [J]. Journal of Tea Science, 2026, 46(2): 191-206.
[2]	CHEN Junrui, HU Junming, SHI Yuanzhi, WEI Xianghua, SONG Chuankui, ZHANG Junhui, ZHENG Fuhai, SUO Guangli. The Effects of Biochar-based Fertilizer on the Physical Stability of Organic Carbon in Soil Aggregates of Tea Gardens [J]. Journal of Tea Science, 2025, 45(5): 865-878.
[3]	MENG Chao, LIANG Tao, ZHANG Xia, WANG Wanhong, DONG Huanglin, LI Ming. Research on Tea Light Complementary Mode Based on Tea Planting and Photovoltaic Power Generation [J]. Journal of Tea Science, 2025, 45(5): 898-908.
[4]	MENG Zhaona, Fida Hussain Magsi, ZHAO Dongxiang, ZHOU You, LI Jianlong, NONG Hongqiu, LONG Yaqin, ZHAO Yuanyan, HUANG Liyun, BIAN Lei, LI Zhaoqun, LUO Zongxiu, XIU Chunli, FU Nanxia, CHEN Zongmao, CAI Xiaoming. Investigation on the Helopeltis (Hemiptera) Species in Tea Gardens of China [J]. Journal of Tea Science, 2025, 45(4): 615-624.
[5]	WU Mengtao, LI Zhaoqun, YANG Yuzhou, MENG Xiangfei, LUO Zongxiu, BIAN Lei, XIU Chunli, FU Nanxia, CHEN Zongmao, WANG Guochang, CAI Xiaoming. The Effect of Operational Parameters of Plant Protection Unmanned Aerial Vehicle on the Droplet Deposition Distribution in Tea Canopy [J]. Journal of Tea Science, 2025, 45(3): 535-544.
[6]	SHEN Shuai, REN Ning, ZHENG Hang, YU Guohong, CHEN Zhidong. Design and Testing of Tea Garden Crawler Plowing Machine [J]. Journal of Tea Science, 2025, 45(2): 273-283.
[7]	JIA Zhijun, JIANG Jiayin, XU Jiajun, LI Yang, DONG Chunwang, SONG Wentao, LI Kai, WEI Chizhang, YAO Yuchen, YAO Lijian, YANG Zidong, LIU Haoyang, MA Rong. Optimization and Testing of Tea Garden Biomimetic Tillage Machine Based on DEM-MBD Coupling Algorithm [J]. Journal of Tea Science, 2025, 45(2): 284-302.
[8]	YAN Yuxiao, ZHOU Dapeng, YANG Yanfen, Xie Jin, LÜ Caiyou, YANG Guangrong, WEN Qinshu. Influence of Organic Planting on Soil Microbial Community Composition and Diversity in Tea Gardens [J]. Journal of Tea Science, 2024, 44(2): 246-260.
[9]	SHEN Xingrong, WANG Qiuhong, HU Qiang, WANG Donghui, FU Shangwen, HAN Wenyan, LI Xin. The Effect of Organic Management on Soil pH in Tea Gardens [J]. Journal of Tea Science, 2024, 44(2): 261-268.
[10]	YANG Yanhu, CHEN Xiaohan, ZHANG Xiaoqing, REN Dajun, ZHANG Shuqin, CHEN Wangsheng. Risk Assessment and Source Analysis of Heavy Metal Pollution in Chinese Tea Gardens in 2000-2022 Based on Meta-analysis [J]. Journal of Tea Science, 2024, 44(1): 37-52.
[11]	TONG Yan, HUANG Hui, WANG Yuhua. Analysis of Codon Usage Bias and Phylogenesis in the Chloroplast Genome of Ancient Tea Tree Camellia taliensis in Forest-tea Garden [J]. Journal of Tea Science, 2023, 43(3): 297-309.
[12]	MA Wanzhu, ZHU Kangying, ZHUO Zhiqing. Effects of Acidification on Mineral Transformation and Potassium Supply Capacity of Tea Garden Soils [J]. Journal of Tea Science, 2023, 43(1): 17-26.
[13]	JIANG Jiayin, DONG Chunwang, NI Yihua, XU Jiajun, LI Yang, MA Rong. Research on Drag Reduction Performance of Tea Garden Bionic Shovel Based on Discrete Element Method [J]. Journal of Tea Science, 2022, 42(6): 791-805.
[14]	LI Yanchun, WANG Hang, LI Zhaowei, YE Jing, WANG Yixiang. Ameliorative Effect of Several Measures on Soil Physicochemical Properties and Microbial Community Structures in Acidified Tea Gardens [J]. Journal of Tea Science, 2022, 42(5): 661-671.
[15]	LIU Yanan, LIU Mengyuan, HUANG Liyun, KANG Zhiwei, XU Yongyu, CHEN Zhenzhen. Analysis of Diversity and Temporal Patterns of the Insect Communities in Tea Gardens [J]. Journal of Tea Science, 2022, 42(1): 109-119.