茶树宜机采表型性状筛选

摘要/Abstract

摘要： 近年来,中国茶叶生产面临劳动力短缺问题,机械化采收技术已成为产业迫切需求。目前机采茶鲜叶破碎率高、质量低,这与茶树表型性状密切相关。以广东省适制红茶或乌龙茶为主的广泛栽培茶树品种为研究对象,采集了7个品种100个样方的表型数据,通过多种分析方法和机采质量预测模型挖掘关键性状。相关性分析发现,机采质量与叶长、叶长宽比、一芽二叶长度和一芽三叶长度呈显著正相关,与总芽密度呈显著负相关。灰色关联分析表明,机采质量与叶长宽比、叶宽、一芽一叶长度和一芽二叶长度高度关联。随机森林分析确定叶长宽比、第三节间长度和第四节间长度分别是影响机采优采率、完整率和破碎率的关键性状。进一步构建了3种核心机采质量性状（优采率、完整率和破碎率）的预测模型,通过4种表型性状集（全部性状、相关性显著、灰色关联度>0.9、随机森林分析前6）与4种机器学习算法的组合验证,发现灰色关联性状预测优采率效果最佳（R²=0.65,RMSE=0.04）;随机森林性状适于预测完整率（R²=0.86,RMSE=0.02）;全部性状预测破碎率精度最优（R²=0.92,RMSE=0.01）。综合建模分析确定一芽二叶长度和第三节间长度是影响红茶和乌龙茶鲜叶原料机采的关键性状,研究结果为宜机采品种选育提供理论依据与关键参数。

关键词: 茶树, 宜机采性状, 表型筛选, 机器学习

Abstract: In recent years, tea production in China has faced challenges of labor shortages, driving urgent industrial demands for mechanized harvesting technologies. The prevalent issues of high leaf fragmentation and low harvest quality in current mechanical systems are closely related to tea plant phenotypic traits. This study investigated seven widely cultivated tea cultivars mainly being suitable for black/oolong tea production in Guangdong Province. Phenotypic data from 100 quadrat sample plots of seven tea cultivars was systematically collected. Multiple analytical approaches, combined with mechanical harvesting quality predictive models, were employed to identify critical determining traits. The correlation analysis demonstrates that leaf length, leaf length-to-width ratio, length of one bud with two leaves, and length of one bud with three leaves were positively correlated with mechanical harvesting quality, whereas the total bud density showed a negative correlation. Grey relational analysis reveals significant associations between the mechanical harvesting quality and the leaf length-to-width ratio, leaf width, length of one bud with one leaf, and length of one bud with two leaves. Random forest analysis identifies the leaf length-to-width ratio, third internode length, and fourth internode length as critical traits influencing the superior harvest rate, intact rate, and fragmentation rate in mechanical harvesting, respectively. Further validation combined four phenotypic trait groups (full trait set, correlation-significant traits, grey relational>0.9, random forest top 6) with four machine learning algorithms to predict three core mechanical harvesting traits. The results demonstrate the grey relational traits achieved the optimal prediction for superior harvest rate (R²=0.65, RMSE=0.04), and random forest-selected traits were suitable for intact rate prediction (R²=0.86, RMSE=0.02), and complete trait group shows the highest fragmentation rate accuracy (R²=0.92, RMSE=0.01). Integrated modeling analysis identifies the length of one bud with two leaves and third internode length as key mechanical harvestable traits influencing black and oolong tea production. These findings established theoretical foundations and provided critical parameters for breeding tea cultivars optimized for mechanical harvesting.

Key words: tea plant, mechanical harvestable traits, phenotypic screening, machine learning

中图分类号:

S571.1
S326

陈家铭, 郭阳, 辜大川, 黎健龙, 陈义勇, 黄燕峰, 唐劲驰, 杨子银. 茶树宜机采表型性状筛选[J]. 茶叶科学, 2025, 45(6): 1006-1020.

CHEN Jiaming, GUO Yang, GU Dachuan, LI Jianlong, CHEN Yiyong, HUANG Yanfeng, TANG Jinchi, YANG Ziyin. Screening of Mechanical Harvestable Traits of Tea Cultivars[J]. Journal of Tea Science, 2025, 45(6): 1006-1020.

参考文献

[1] 阮建云. 中国茶树栽培40年[J]. 中国茶叶, 2019, 41(7): 1-7.
Ruan J Y.Forty years of tea cultivation in China[J]. China Tea, 2019, 41(7): 1-7.
[2] 陈根生, 尹军峰, 袁海波, 等. 往复切割式机采鲜叶品质特点及其名优绿茶适制品类[J]. 中国茶叶, 2018, 40(7): 1-5.
Chen G S, Yin J F, Yuan H B, et al.Quality characteristics of fresh tea leaves harvested by reciprocating cutter and suitable types for famous green tea[J]. China Tea, 2018, 40(7): 1-5.
[3] 郭雅玲, 赖凌凌, 刘亚峰, 等. 乌龙茶机采及其分选技术研究进展[J]. 中国农机化学报, 2016, 37(1): 262-267.
Guo Y L, Lai L L, Liu Y F, et al.Research advances on technologies of mechanical-plucking Oolong tea and screening[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(1): 262-267.
[4] 汤一平, 韩旺明, 胡安国, 等. 基于机器视觉的乘用式智能采茶机设计与试验[J]. 农业机械学报, 2016, 47(7): 15-20.
Tang Y P, Han W M, Hu A G, et al.Design and experiment of intelligentized yea-plucking machine for human riding based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(7): 15-20.
[5] Weng X X, Tan D P, Wang G, et al.CFD simulation and optimization of the leaf collecting mechanism for the riding-type tea plucking machine[J]. Agriculture, 2023, 13(5): 946. doi: 10.3390/agriculture13050946.
[6] 李林山. 茶叶采摘技术及采茶机械研究进展[J]. 南方农机, 2024, 55(13): 61-64.
Li L S.Research progress on tea picking technology and machinery[J]. Southern Agricultural Machinery, 2024, 55(13): 61-64.
[7] 罗泽涌, 陈建, 方晶晶, 等. 我国丘陵山区茶园种植机械化现状与发展研究[J]. 农机化研究, 2020, 42(2): 1-7.
Luo Z Y, Chen J, Fang J J, et al.Current situation and development suggestions of tea garden planting mechanization in hilly and mountainous areas[J]. Journal of Agricultural Mechanization Research, 2020, 42(2): 1-7.
[8] 黄藩, 王云, 熊元元, 等. 我国茶叶机械化采摘技术研究现状与发展趋势[J]. 江苏农业科学, 2019, 47(12): 48-51.
Huang F, Wang Y, Xiong Y Y, et al.Research status and development trends of mechanized tea picking technology in China[J]. Jiangsu Agricultural Sciences, 2019, 47(12): 48-51.
[9] 吴焕焕, 任志红, 肖文敏, 等. 山东茶区不同茶树品种的机采适宜性评价[J]. 中国农学通报, 2025, 41(1): 76-81.
Wu H H, Ren Z H, Xiao W M, et al.Evaluation on mechanized-picking suitability of different tea cultivars in Shandong province[J]. Chinese Agricultural Science Bulletin, 2025, 41(1): 76-81.
[10] Yang H L, Chen L, Ma Z B, et al.Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator[J]. Computers and Electronics in Agriculture, 2021, 181: 105946. doi: 10.1016/j.compag.2020.105946.
[11] Zhang L, Zou L, Wu C Y, et al.Method of famous tea sprout identification and segmentation based on improved watershed algorithm[J]. Computers and Electronics in Agriculture, 2021, 184: 106108. doi: 10.1016/j.compag.2021.106108.
[12] 沈宝国, 董春旺, 蒋修定. 茶树鲜叶分选研究现状与展望[J]. 中国农机化学报, 2016, 37(8): 87-90.
Shen B G, Dong C W, Jiang X D.Research status and prospects of fresh tea leaf sorting[J]. Journal of Chinese Agricultural Mechanization, 2016, 37(8): 87-90.
[13] 骆耀平, 宋婷婷, 文东华, 等. 茶树新梢节间与展叶角度生长变化及对名优茶机采的影响[J]. 浙江大学学报(农业与生命科学版), 2009, 35(4): 420-424.
Luo Y P, Song T T, Wen D H, et al.Growth changes of internode length and leaf spreading angle in tea shoots and their effects on mechanical harvesting of high-quality tea[J]. Journal of Zhejiang University (Agriculture and Life Sciences), 2009, 35(4): 420-424.
[14] 陈红旭, 唐茜, 邹瑶, 等. 四川茶区适宜机采茶树品种的筛选[J]. 中国茶叶, 2019, 41(2): 23-27, 31.
Chen H X, Tang Q, Zou Y, et al.Screening of tea cultivars suitable for mechanical harvesting in Sichuan tea region[J]. China Tea, 2019, 41(2): 23-27, 31.
[15] Luo Y, Yu Q Q, Xie Y H, et al.Internode length is correlated with GA3 content and is crucial to the harvesting performance of tea-picking machines[J]. Plants, 2023, 12(13): 2508. doi: 10.3390/plants12132508.
[16] Wang Z H, Peng H, Yue C N, et al.Selection of core evaluation indices and construction of a comprehensive evaluation method for machine-harvested tea plant cultivars[J]. Euphytica, 2022, 218(11): 162. doi: 10.1007/s10681-022-03112-x.
[17] 虞富莲. 《中国古茶树》[M]. 昆明: 云南科技出版社, 2016.
Yu F L.Ancient tea trees in China [M]. Kunming: Yunnan Science and Technology Press, 2016.
[18] 赖幸菲, 赖兆祥, 操君喜, 等. 英红九号茶园机械化采摘关键技术研究[J]. 广东农业科学, 2020, 47(9): 127-133.
Lai X F, Lai Z X, Cao J X, et al.Study on key technologies of mechanical picking in Yinghong No. 9 tea garden[J]. Guangdong Agricultural Sciences, 2020, 47(9): 127-133.
[19] 樊闯, 赵子皓, 张雪松, 等. 基于BP神经网络的一季稻发育期预测模型[J]. 浙江农业学报, 2023, 35(2): 434-444.
Fan C, Zhao Z H, Zhang X S, et al.Prediction model of one season rice development period based on BP neural network[J]. Acta Agriculturae Zhejiangensis, 2023, 35(2): 434-444.
[20] 马楠, 蔡朝朝, 白涛. 基于机器学习的新疆植被覆盖变化及其影响因子之间的关系[J]. 湖北农业科学, 2024, 63(8): 216-222.
Ma N, Cai C C, Bai T.The relationship between vegetation cover change and its influencing factors in Xinjiang based on machine learning[J]. Hubei Agricultural Sciences, 2024, 63(8): 216-222.
[21] 唐翠翠, 黄文江, 罗菊花, 等. 基于相关向量机的冬小麦蚜虫遥感预测[J]. 农业工程学报, 2015, 31(6): 201-207.
Tang C C, Huang W J, Luo J H, et al.Remote sensing prediction of winter wheat aphids based on relevance vector machine[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(6): 201-207.
[22] 鲁军景, 孙雷刚, 黄文江. 作物病虫害遥感监测和预测预警研究进展[J]. 遥感技术与应用, 2019, 34(1): 21-32.
Lu J J, Sun L G, Huang W J.Advances in remote sensing monitoring and early warning of crop pests and diseases[J]. Remote Sensing Technology and Application, 2019, 34(1): 21-32.
[23] 刘雪梅, 章海亮. 基于近红外光谱的不同建模方法检测土壤有机质和速效P含量的研究[J]. 西北农林科技大学学报(自然科学版), 2013, 41(4): 52-56, 68.
Liu X M, Zhang H L.Measurement of soil organic matter and phosphorus using different calibration methods based on near-infrared spectroscopy[J]. Journal of Northwest A & F University (Natural Science Edition), 2013, 41(4): 52-56, 68.
[24] 陈治瑀. 基于无人机技术下农作物产量估算模型研究[J]. 农机使用与维修, 2024(7): 148-151.
Chen Z Y.Research on crop yield estimation modeling based on unmanned aerial vehicle technology[J]. Agricultural Machinery Use & Maintenance, 2024(7): 148-151.
[25] Chen Y T, Chen S F.Localizing plucking points of tea leaves using deep convolutional neural networks[J]. Computers and Electronics in Agriculture, 2020, 171: 105298. doi: 10.1016/j.compag.2020.105298.
[26] 向芬, 刘红艳, 戴翠婷, 等. 不同修剪方式对春季茶树生长与机采效果的影响[J]. 中国茶叶, 2024, 46(12): 54-58.
Xiang F, Liu H Y, Dai C T, et al.Effects of different pruning methods on tea plant growth and mechanized picking in spring[J]. China Tea, 2024, 46(12): 54-58.
[27] 游小妹, 陈志辉, 钟秋生, 等. 8个适宜机采乌龙茶品种各叶位节间长与展叶角度分析[J]. 茶叶学报, 2017, 58(1): 21-25.
You X M, Chen Z H, Zhong Q S, et al.Internode-distances and leaf-angles of eight oolong tea cultivars with potential for mechanical picking[J]. Acta Tea Sinica, 2017, 58(1): 21-25.
[28] 邢瑶, 唐锁海, 梅菊芬, 等. 不同茶树品种机采大宗茶鲜叶比较研究[J]. 中国茶叶, 2020, 42(4): 36-39.
Xing Y, Tang S H, Mei J F, et al.A comparative study of freshly harvested bulk tea leaves from different tea cultivars[J]. China Tea, 2020, 42(4): 36-39.
[29] Madamombe G, Tesfamariam E, Taylor N.Yield decline in mechanically harvested clonal tea (Camellia sinensis (L.) O. Kuntze) as influenced by changes in source/sink and radiation interception dynamics in the canopy[J]. Scientia Horticulturae, 2015, 194: 286-294. doi: 10.1016/j.scienta.2015.08.009.
[30] 郑旭霞, 敖存, 毛宇骁, 等. 杭州名优茶机采品种筛选初步研究[J]. 浙江农业科学, 2016, 57(5): 661-663.
Zheng X X, Ao C, Mao Y X, et al.Preliminary study on screening machine-harvested cultivars for Hangzhou high-quality tea[J]. Journal of Zhejiang Agricultural Sciences, 2016, 57(5): 661-663.
[31] Liu H R, Duan L X, Ma J Q, et al. CsEXL3 regulate mechanical harvest-related droopy leaves under the transcriptional activation of CsBES1.2 in tea plant [J]. Horticulture Research, 2024, 11(5): uhae074. doi: 10.1093/hr/uhae074.
[32] Zhu J P, Li X M, Huang J Y, et al.Transcriptomics and plant hormone analysis reveal the mechanism of branching angle formation in tea plants (Camellia sinensis)[J]. International Journal of Molecular Sciences, 2025, 26(2): 604. doi: 10.3390/ijms26020604.
[33] 冯花, 王飞权, 陈荣冰, 等. 不同来源地茶树种质资源表型性状遗传多样性分析[J]. 热带作物学报, 2021, 42(10): 2758-2768.
Feng H, Wang F Q, Chen R B, et al.Genetic diversity analysis of phenotypic characters of tea germplasm resources from different origins[J]. Chinese Journal of Tropical Crops, 2021, 42(10): 2758-2768.
[34] Kong W L, Kong X R, Xia Z Q, et al.Genomic analysis of 1,325 Camellia accessions sheds light on agronomic and metabolic traits for tea plant improvement[J]. Nature Genetics, 2025, 57: 997-1007. doi: 10.1038/s41588-025-02135-z.
[35] Liu H R, Duan L X, Tang C Q, et al. A single base mutation in promoter of CsTPR enhances the negative regulation on mechanical related leaves droopiness in tea plant [J]. Horticulture Research, 2025, 12(17): uhaf098. doi: 10.1093/hr/uhaf098.
[36] Xia X B, Mi X Z, Jin L, et al.CsLAZY1 mediates shoot gravitropism and branch angle in tea plants (Camellia sinensis)[J]. BMC Plant Biology, 2021, 21(1): 243. doi: 10.1186/s12870-021-03044-z.
[37] Lu L, Luo W W, Zheng Y N, et al.Effect of different pruning operations on the plant growth, phytohormones and transcriptome profiles of the following spring tea shoots[J]. Beverage Plant Research, 2022, 2: 12. doi: 10.48130/BPR-2022-0012.
[38] Li W, Xiang F, Su Y, et al.Gibberellin increases the bud yield and theanine accumulation in Camellia sinensis (L.) Kuntze[J]. Molecules, 2021, 26(11): 3290. doi: 10.3390/molecules26113290.
[39] Chen J M, Wu S H, Mao K Q, et al.Adverse effects of shading on the tea yield and the restorative effects of exogenously applied brassinolide[J]. Industrial Crops and Products, 2023, 197: 116546. doi: 10.1016/j.indcrop.2023.116546.
[40] Mao K Q, Li J L, Wu S H, et al.Melatonin treatment promotes cold adaptation and spring growth of tea plants[J]. Industrial Crops and Products, 2023, 200: 116834. doi: 10.1016/j.indcrop.2023.116834.