[1]

Yu X, Xiao J, Chen S, Yu Y, Ma J, et al. 2020. Metabolite signatures of diverse Camellia sinensis tea populations. Nature Communications 11:5586

doi: 10.1038/s41467-020-19441-1
[2]

Yang G, Meng Q, Shi J, Zhou M, Zhu Y, et al. 2023. Special tea products featuring functional components: health benefits and processing strategies. Comprehensive Reviews in Food Science and Food Safety 22:1686−721

doi: 10.1111/1541-4337.13127
[3]

Zhao F, Chen M, Jin S, Wang S, Yue W, et al. 2022. Macro-composition quantification combined with metabolomics analysis uncovered key dynamic chemical changes of aging white tea. Food Chemistry 366:130593

doi: 10.1016/j.foodchem.2021.130593
[4]

Gao T, Shao S, Hou B, Hong Y, Ren W, et al. 2023. Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China. Beverage Plant Research 3:17

doi: 10.48130/BPR-2023-0017
[5]

Peng Y, Zheng C, Guo S, Gao F, Wang X, et al. 2023. Metabolomics integrated with machine learning to discriminate the geographic origin of Rougui Wuyi rock tea. NPJ Science of Food 7:7

doi: 10.1038/s41538-023-00187-1
[6]

Wang L, Yao L, Hao X, Li N, Wang Y, et al. 2019. Transcriptional and physiological analyses reveal the association of ROS metabolism with cold tolerance in tea plant. Environmental and Experimental Botany 160:45−58

doi: 10.1016/j.envexpbot.2018.11.011
[7]

Shen J, Wang Y, Chen C, Ding Z, Hu J, et al. 2015. Metabolite profiling of tea (Camellia sinensis L.) leaves in winter. Scientia Horticulturae 192:1−9

doi: 10.1016/j.scienta.2015.05.022
[8]

Chen S, Shen J, Fan K, Qian W, Gu H, et al. 2022. Hyperspectral machine-learning model for screening tea germplasm resources with drought tolerance. Frontiers in Plant Science 13:1048442

doi: 10.3389/fpls.2022.1048442
[9]

Liu J, Zhang C, Hu R, Zhu X, Cai J. 2019. Aging of agricultural labor force and technical efficiency in tea production: evidence from Meitan County, China. Sustainability 11:6246

doi: 10.3390/su11226246
[10]

Xie S, Feng H, Yang F, Zhao Z, Hu X, et al. 2019. Does dual reduction in chemical fertilizer and pesticides improve nutrient loss and tea yield and quality? A pilot study in a green tea garden in Shaoxing, Zhejiang Province, China. Environmental Science and Pollution Research 26:2464−76

doi: 10.1007/s11356-018-3732-1
[11]

Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. 2018. Machine learning in agriculture: a review. Sensors 18:2674

doi: 10.3390/s18082674
[12]

Kasinathan T, Singaraju D, Uyyala SR. 2021. Insect classification and detection in field crops using modern machine learning techniques. Information Processing in Agriculture 8:446−57

doi: 10.1016/j.inpa.2020.09.006
[13]

Sujatha R, Chatterjee JM, Jhanjhi N, Brohi SN. 2021. Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems 80:103615

doi: 10.1016/j.micpro.2020.103615
[14]

van Klompenburg T, Kassahun A, Catal C. 2020. Crop yield prediction using machine learning: a systematic literature review. Computers and Electronics in Agriculture 177:105709

doi: 10.1016/j.compag.2020.105709
[15]

Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, et al. 2021. Machine learning in agriculture: a comprehensive updated review. Sensors 21:3758

doi: 10.3390/s21113758
[16]

Hamrani A, Akbarzadeh A, Madramootoo CA. 2020. Machine learning for predicting greenhouse gas emissions from agricultural soils. Science of The Total Environment 741:140338

doi: 10.1016/j.scitotenv.2020.140338
[17]

Wang H, Gu J, Wang M. 2023. A review on the application of computer vision and machine learning in the tea industry. Frontiers in Sustainable Food Systems 7:1172543

doi: 10.3389/fsufs.2023.1172543
[18]

Xu Q, Zhou Y, Wu L. 2024. Advancing tea detection with artificial intelligence: strategies, progress, and future prospects. Trends in Food Science & Technology 153:104731

doi: 10.1016/j.jpgs.2024.104731
[19]

Wei Y, Wen Y, Huang X, Ma P, Wang L, et al. 2024. The dawn of intelligent technologies in tea industry. Trends in Food Science & Technology 144:104337

doi: 10.1016/j.jpgs.2024.104337
[20]

Wang P, Fan E, Wang P. 2021. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. Pattern Recognition Letters 141:61−67

doi: 10.1016/j.patrec.2020.07.042
[21]

Wang J, Ma Y, Zhang L, Gao RX, Wu D. 2018. Deep learning for smart manufacturing: methods and applications. Journal of Manufacturing Systems 48:144−56

doi: 10.1016/j.jmsy.2018.01.003
[22]

Khan S, Sajjad M, Hussain T, Ullah A, Imran AS. 2020. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. IEEE Access 9:10657−73

doi: 10.1109/ACCESS.2020.3048172
[23]

Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. 2020. Introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology 9:14

doi: 10.1167/tvst.9.2.14
[24]

Wu D, Yang H, Chen X, He Y, Li X. 2008. Application of image texture for the sorting of tea categories using multi-spectral imaging technique and support vector machine. Journal of Food Engineering 88:474−83

doi: 10.1016/j.jfoodeng.2008.03.005
[25]

Wang S, Yang X, Zhang Y, Phillips P, Yang J, et al. 2015. Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17:6663−82

doi: 10.3390/e17106663
[26]

Liu L, Hu P, Yang F, Song M. 2020. Application of terahertz time-domain spectroscopy combined with support vector machine to determine tea and pesticide samples. Materials Express 10:1646−53

doi: 10.1166/mex.2020.1820
[27]

Ahmad H, Sun J, Nirere A, Shaheen N, Zhou X, et al. 2021. Classification of tea varieties based on fluorescence hyperspectral image technology and ABC-SVM algorithm. Journal of Food Processing and Preservation 45:e15241

doi: 10.1111/jfpp.15241
[28]

Hossain S, Mou RM, Hasan MM, Chakraborty S, Razzak MA. 2018. Recognition and detection of tea leaf's diseases using support vector machine. Proc. of 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), Penang, Malaysia, 2018. pp. 150−54. US: IEEE. doi: 10.1109/CSPA.2018.8368703

[29]

Prabu S, Bapu BRT, Sridhar S, Nagaraju V. 2022. Tea plant leaf disease identification using hybrid filter and support vector machine classifier technique. In Recent Advances in Internet of Things and Machine Learning, eds Balas VE, Solanki VK, Kumar R. Cham: Springer. Volume 215. pp. 117−28 doi: 10.1007/978-3-030-90119-6_10

[30]

Ren G, Zhang X, Wu R, Yin L, Hu W, et al. 2023. Rapid characterization of black tea taste quality using miniature NIR spectroscopy and electronic tongue sensors. Biosensors 13:92

doi: 10.3390/bios13010092
[31]

Amsaraj R, Mutturi S. 2023. Rapid detection of sunset yellow adulteration in tea powder with variable selection coupled to machine learning tools using spectral data. Journal of Food Science and Technology 60:1530−40

doi: 10.1007/s13197-023-05694-3
[32]

Liang L, Wang J, Deng F, Kong D. 2023. Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM). PLoS One 18:e0263969

doi: 10.1371/journal.pone.0263969
[33]

Jui SJJ, Masrur Ahmed AA, Bose A, Raj N, Sharma E, et al. 2022. Spatiotemporal hybrid random forest model for tea yield prediction using satellite-derived variables. Remote Sensing 14:805

doi: 10.3390/rs14030805
[34]

Dao DH, Tang NC, Pham BT. Monitoring and evaluating the fermentation level of black tea using the random forest model. In Advances in Engineering Research and Application, eds Nguyen DC, Vu NP, Long BT, Puta H, Sattler KU. Cham: Springer. pp. 739–53 doi: 10.1007/978-3-030-92574-1_76

[35]

Deng X, Liu Z, Zhan Y, Ni K, Zhang Y, et al. 2020. Predictive geographical authentication of green tea with protected designation of origin using a random forest model. Food Control 107:106807

doi: 10.1016/j.foodcont.2019.106807
[36]

Han Y, He Y, Liang Z, Shi G, Zhu X, et al. 2023. Risk assessment and application of tea frost hazard in Hangzhou City based on the random forest algorithm. Agriculture 13:327

doi: 10.3390/agriculture13020327
[37]

Diniz PHGD, Pistonesi MF, Alvarez MB, Band BSF, de Araújo MCU. 2015. Simplified tea classification based on a reduced chemical composition profile via successive projections algorithm linear discriminant analysis (SPA-LDA). Journal of Food Composition and Analysis 39:103−10

doi: 10.1016/j.jfca.2014.11.012
[38]

Mohammadi N, Esteki M, Simal-Gandara J. 2024. Machine learning for authentication of black tea from narrow-geographic origins: combination of PCA and PLS with LDA and SVM classifiers. LWT 203:116401

doi: 10.1016/j.lwt.2024.116401
[39]

Lin J, Zhang P, Pan Z, Xu H, Luo Y, et al. 2013. Discrimination of oolong tea (Camellia sinensis) varieties based on feature extraction and selection from aromatic profiles analysed by HS-SPME/GC-MS. Food Chemistry 141:259−65

doi: 10.1016/j.foodchem.2013.02.128
[40]

Gan N, Sun M, Lu C, Li M, Wang Y, et al. 2022. High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm. Journal of the Science of Food and Agriculture 102:6858−67

doi: 10.1002/jsfa.12047
[41]

Hu Y, Xu L, Huang P, Luo X, Wang P, et al. 2021. Reliable identification of oolong tea species: nondestructive testing classification based on fluorescence hyperspectral technology and machine learning. Agriculture 11:1106

doi: 10.3390/agriculture11111106
[42]

Wu X, Yang J, Wang S. 2018. Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm. Multimedia Tools and Applications 77:3745−59

doi: 10.1007/s11042-016-3931-z
[43]

Wijaya DR, Handayani R, Fahrudin T, Kusuma GP, Afianti F. 2024. Electronic nose and optimized machine learning algorithms for noninfused aroma-based quality identification of gambung green tea. IEEE Sensors Journal 24:1880−93

doi: 10.1109/JSEN.2023.3337264
[44]

Xu M, Wang J, Zhu L. 2021. Tea quality evaluation by applying E-nose combined with chemometrics methods. Journal of Food Science and Technology 58:1549−61

doi: 10.1007/s13197-020-04667-0
[45]

Hu Y, Huang P, Wang Y, Sun J, Wu Y, et al. 2023. Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis. Journal of Food Composition and Analysis 117:105136

doi: 10.1016/j.jfca.2023.105136
[46]

Shao P, Wu M, Wang X, Zhou J, Liu S. 2018. Research on the tea bud recognition based on improved k-means algorithm. MATEC Web of Conferences 232:03050

doi: 10.1051/matecconf/201823203050
[47]

Shevchuk A, Jayasinghe L, Kuhnert N. 2018. Differentiation of black tea infusions according to origin, processing and botanical varieties using multivariate statistical analysis of LC-MS data. Food Research International 109:387−402

doi: 10.1016/j.foodres.2018.03.059
[48]

Zhu X, Goldberg AB. 2009. Overview of semi-supervised learning. In Introduction to Semi-Supervised Learning. Cham: Springer. pp. 9–19 doi: 10.1007/978-3-031-01548-9_2

[49]

Weng Y, Zhang Y, Wang W, Dening T. 2024. Semi-supervised information fusion for medical image analysis: recent progress and future perspectives. Information Fusion 106:102263

doi: 10.1016/j.inffus.2024.102263
[50]

Kondratovich E, Baskin II, Varnek A. 2013. Transductive support vector machines: promising approach to model small and unbalanced datasets. Molecular Informatics 32:261−66

doi: 10.1002/minf.201200135
[51]

Yang J, Chen Y. 2022. Tender leaf identification for early-spring green tea based on semi-supervised learning and image processing. Agronomy 12:1958

doi: 10.3390/agronomy12081958
[52]

Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, et al. 2015. Human-level control through deep reinforcement learning. Nature 518:529−33

doi: 10.1038/nature14236
[53]

DeepSeek-AI, Guo D, Yang D, Zhang H, Song J, et al. 2025. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. arXiv 00:2501.12948

doi: 10.48550/arXiv.2501.12948
[54]

Lin G, Xiong J, Zhao R, Li X, Hu H, et al. 2023. Efficient detection and picking sequence planning of tea buds in a high-density canopy. Computers and Electronics in Agriculture 213:108213

doi: 10.1016/j.compag.2023.108213
[55]

LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436−44

doi: 10.1038/nature14539
[56]

Gayathri S, Wise DCJW, Shamini PB, Muthukumaran N. 2020. Image analysis and detection of tea leaf disease using deep learning. Proc of 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2020. US: IEEE. pp. 398−403. doi: 10.1109/ICESC48915.2020.9155850

[57]

Deepa C, Sanjay SM, Mosses V, Yugeshwaran M, Sam Jebaraj A. 2024. Camellia sinensis (tea) plant disease classification using RESNET. Proc of 2024 International Conference on Science Technology Engineering and Management (ICSTEM), Coimbatore, India, 2024. US: IEEE. pp. 1−4 doi: 10.1109/ICSTEM61137.2024.10560945

[58]

Bai B, Wang J, Li J, Yu L, Wen J, et al. 2024. T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea plantation environments. Journal of the Science of Food and Agriculture 104:5698−711

doi: 10.1002/jsfa.13396
[59]

Shi M, Zheng D, Wu T, Zhang W, Fu R, et al. 2024. Small object detection algorithm incorporating swin transformer for tea buds. PLoS One 19:e0299902

doi: 10.1371/journal.pone.0299902
[60]

Xue Z, Xu R, Bai D, Lin H. 2023. YOLO-tea: a tea disease detection model improved by YOLOv5. Forests 14:415

doi: 10.3390/f14020415
[61]

Ye R, Shao G, Yang Z, Sun Y, Gao Q, et al. 2024. Detection model of tea disease severity under low light intensity based on YOLOv8 and EnlightenGAN. Plants 13:1377

doi: 10.3390/plants13101377
[62]

Li H, Shi H, Du A, Mao Y, Fan K, et al. 2022. Symptom recognition of disease and insect damage based on Mask R-CNN, wavelet transform, and F-RNet. Frontiers in Plant Science 13:922797

doi: 10.3389/fpls.2022.922797
[63]

Masoud KM, Persello C, Tolpekin VA. 2020. Delineation of agricultural field boundaries from sentinel-2 images using a novel super-resolution contour detector based on fully convolutional networks. Remote Sensing 12:59

doi: 10.3390/rs12010059
[64]

Chen YT, Chen SF. 2020. Localizing plucking points of tea leaves using deep convolutional neural networks. Computers and Electronics in Agriculture 171:105298

doi: 10.1016/j.compag.2020.105298
[65]

Lin YK, Chen SF, Kuo YF, Liu TL, Lee SY. 2021. Developing a guiding and growth status monitoring system for riding-type tea plucking machine using fully convolutional networks. Computers and Electronics in Agriculture 191:106540

doi: 10.1016/j.compag.2021.106540
[66]

Zhu N, Liu X, Liu Z, Hu K, Wang Y, et al. 2018. Deep learning for smart agriculture: concepts, tools, applications, and opportunities. International Journal of Agricultural and Biological Engineering 11:32−44

doi: 10.25165/j.ijabe.20181104.4475
[67]

Yao Z, Zhu X, Zeng Y, Qiu X. 2023. Extracting tea plantations from multitemporal Sentinel-2 images based on deep learning networks. Agriculture 13:10

doi: 10.3390/agriculture13010010
[68]

Mao Y, Li H, Xu Y, Wang S, Yin X, et al. 2024. Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology. Journal of the Science of Food and Agriculture 104:9336−48

doi: 10.1002/jsfa.13756
[69]

Li H, Mao Y, Wang Y, Fan K, Shi H, et al. 2022. Environmental simulation model for rapid prediction of tea seedling growth. Agronomy 12:3165

doi: 10.3390/agronomy12123165
[70]

Huang Y, Jiang H, Wang W. 2022. Research on tea tree growth monitoring model using soil information. Plants 11:262

doi: 10.3390/plants11030262
[71]

Chen X, Hassan MM, Yu J, Zhu A, Han Z, et al. 2024. Time series prediction of insect pests in tea gardens. Journal of the Science of Food and Agriculture 104:5614−24

doi: 10.1002/jsfa.13393
[72]

Krishnan Jayapal S, Poruran S. 2023. Enhanced disease identification model for tea plant using deep learning. Intelligent Automation & Soft Computing 35:1261−75

doi: 10.32604/iasc.2023.026564
[73]

Zhang J, Guo H, Guo J, Zhang J. 2023. An information entropy masked vision transformer (IEM-ViT) model for recognition of tea diseases. Agronomy 13:1156

doi: 10.3390/agronomy13041156
[74]

Zilvan V, Ramdan A, Heryana A, Krisnandi D, Suryawati E, et al. 2022. Convolutional variational autoencoder-based feature learning for automatic tea clone recognition. Journal of King Saud University - Computer and Information Sciences 34:3332−42

doi: 10.1016/j.jksuci.2021.01.020
[75]

Cimpoiu C, Cristea VM, Hosu A, Sandru M, Seserman L. 2011. Antioxidant activity prediction and classification of some teas using artificial neural networks. Food Chemistry 127:1323−28

doi: 10.1016/j.foodchem.2011.01.091
[76]

Kalathingal MSH, Basak S, Mitra J. 2020. Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. Journal of Food Process Engineering 43:e13128

doi: 10.1111/jfpe.13128
[77]

Chen Q, Zhao J, Vittayapadung S. 2008. Identification of the green tea grade level using electronic tongue and pattern recognition. Food Research International 41:500−04

doi: 10.1016/j.foodres.2008.03.005
[78]

Li X, He Y. 2008. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks. Biosystems Engineering 99:313−21

doi: 10.1016/j.biosystemseng.2007.11.007
[79]

Mienye ID, Sun Y. 2022. A survey of ensemble learning: concepts, algorithms, applications, and prospects. IEEE Access 10:99129−49

doi: 10.1109/ACCESS.2022.3207287
[80]

Geng J, Li H, Luan W, Shi Y, Pang J, et al. 2023. Estimation of daily actual evapotranspiration of tea plantations using ensemble machine learning algorithms and six available scenarios of meteorological data. Applied Sciences 13:12961

doi: 10.3390/app132312961
[81]

Raza A, Hu Y, Lu Y. 2024. Improving carbon flux estimation in tea plantation ecosystems: a machine learning ensemble approach. European Journal of Agronomy 160:127297

doi: 10.1016/j.eja.2024.127297
[82]

Li J, Li Q, Luo W, Zeng L, Luo L. 2024. Rapid color quality evaluation of needle-shaped green tea using computer vision system and machine learning models. Foods 13:2516

doi: 10.3390/foods13162516
[83]

Zou Y, Ma W, Tang Q, Xu W, Tan L, et al. 2020. A high-precision method evaluating color quality of Sichuan Dark Tea based on colorimeter combined with multi-layer perceptron. Journal of Food Process Engineering 43:e13444

doi: 10.1111/jfpe.13444
[84]

Liu H, Yu D, Gu Y. 2019. Classification and evaluation of quality grades of organic green teas using an electronic nose based on machine learning algorithms. IEEE Access 7:172965−73

doi: 10.1109/ACCESS.2019.2957112
[85]

Xu M, Wang J, Zhu L. 2019. The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics. Food Chemistry 289:482−89

doi: 10.1016/j.foodchem.2019.03.080
[86]

Liang J, Guo J, Xia H, Ma C, Qiao X. 2025. A black tea quality testing method for scale production using CV and NIRS with TCN for spectral feature extraction. Food Chemistry 464:141567

doi: 10.1016/j.foodchem.2024.141567
[87]

Ren G, Wang Y, Ning J, Zhang Z. 2021. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. Journal of the Science of Food and Agriculture 101:2135−42

doi: 10.1002/jsfa.10836
[88]

Li L, Xie S, Ning J, Chen Q, Zhang Z. 2019. Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems. Journal of the Science of Food and Agriculture 99:1787−94

doi: 10.1002/jsfa.9371
[89]

Yang B, Qi L, Wang M, Hussain S, Wang H, et al. 2020. Cross-category tea polyphenols evaluation model based on feature fusion of electronic nose and hyperspectral imagery. Sensors 20:50

doi: 10.3390/s20010050
[90]

Yang H, Chen L, Chen M, Ma Z, Deng F, et al. 2019. Tender tea shoots recognition and positioning for picking robot using improved YOLO-V3 model. IEEE Access 7:180998−1011

doi: 10.1109/ACCESS.2019.2958614
[91]

Jayanthy S, Sathyendraa VM, Sumedh KP, Suresh S. 2023. Tea leaf disease classification and tea bud identification. Proc. 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, India, 2022. US: IEEE. pp. 1−5 doi: 10.1109/ICERECT56837.2022.10059683

[92]

Wang G, Wang Z, Zhao Y, Zhang Y. 2022. Tea bud recognition based on machine learning. Proc. 2022 41st Chinese Control Conference (CCC), Hefei, China, 2022. US: IEEE. pp. 6533−37 doi: 10.23919/CCC55666.2022.9902610

[93]

Chen C, Lu J, Zhou M, Yi J, Liao M, et al. 2022. A YOLOv3-based computer vision system for identification of tea buds and the picking point. Computers and Electronics in Agriculture 198:107116

doi: 10.1016/j.compag.2022.107116
[94]

Wang T, Zhang K, Zhang W, Wang R, Wan S, et al. 2023. Tea picking point detection and location based on Mask-RCNN. Information Processing in Agriculture 10:267−75

doi: 10.1016/j.inpa.2021.12.004
[95]

Hassoun A, Aït-Kaddour A, Abu-Mahfouz AM, Rathod NB, Bader F, et al. 2023. The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies. Critical Reviews in Food Science and Nutrition 63:6547−63

doi: 10.1080/10408398.2022.2034735
[96]

Batool D, Shahbaz M, Shahzad Asif H, Shaukat K, Alam TM, et al. 2022. A hybrid approach to tea crop yield prediction using simulation models and machine learning. Plants 11:1925

doi: 10.3390/plants11151925
[97]

Xu H, Ma W, Tan Y, Liu X, Zheng Y, et al. 2022. Yield estimation method for tea based on YOLOv5 deep learning. Journal of China Agricultural University 27:213−20

doi: 10.11841/j.issn.1007-4333.2022.12.18
[98]

Liu H, Liu Y, Xu W, Wu M, Wang L, et al. 2025. A seasonal fresh tea yield estimation method with machine learning algorithms at field scale integrating UAV RGB and Sentinel-2 imagery. Plants 14:373

doi: 10.3390/plants14030373
[99]

Huang Y. 2023. Improved SVM-based soil-moisture-content prediction model for tea plantation. Plants 12:2309

doi: 10.3390/plants12122309
[100]

Xing W, Zhou C, Li J, Wang W, He J, et al. 2022. Suitability evaluation of tea cultivation using machine learning technique at town and village scales. Agronomy 12:2010

doi: 10.3390/agronomy12092010
[101]

Sun J, Zhou X, Hu Y, Wu X, Zhang X, et al. 2019. Visualizing distribution of moisture content in tea leaves using optimization algorithms and NIR hyperspectral imaging. Computers and Electronics in Agriculture 160:153−59

doi: 10.1016/j.compag.2019.03.004
[102]

Li H, Wang Y, Fan K, Mao Y, Shen Y, et al. 2022. Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data. Frontiers in Plant Science 13:898962

doi: 10.3389/fpls.2022.898962
[103]

Jiang J, Ji H, Zhou G, Pan R, Zhao L, et al. 2025. Non-destructive monitoring of tea plant growth through UAV spectral imagery and meteorological data using machine learning and parameter optimization algorithms. Computers and Electronics in Agriculture 229:109795

doi: 10.1016/j.compag.2024.109795
[104]

Chen J, Liu Q, Gao L. 2019. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11:343

doi: 10.3390/sym11030343
[105]

Heng Q, Yu S, Zhang Y. 2024. A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling. Heliyon 10:e26465

doi: 10.1016/j.heliyon.2024.e26465
[106]

Sun Y, Jiang Z, Zhang L, Dong W, Rao Y. 2019. SLIC_SVM based leaf diseases saliency map extraction of tea plant. Computers and Electronics in Agriculture 157:102−9

doi: 10.1016/j.compag.2018.12.042
[107]

Hu G, Wang H, Zhang Y, Wan M. 2021. Detection and severity analysis of tea leaf blight based on deep learning. Computers & Electrical Engineering 90:107023

doi: 10.1016/j.compeleceng.2021.107023
[108]

Hu G, Wei K, Zhang Y, Bao W, Liang D. 2021. Estimation of tea leaf blight severity in natural scene images. Precision Agriculture 22:1239−62

doi: 10.1007/s11119-020-09782-8
[109]

Deng X, Photong C. 2024. Evaluation of tea leaf disease identification based on convolutional neural networks VGG16, ResNet50, and DenseNet169 image recognitions. 2024 12th International Electrical Engineering Congress, Pattaya, Thailand, 2024. US: IEEE. pp. 1−4 doi: 10.1109/iEECON60677.2024.10537865

[110]

Chen J, Liu Q, Gao L. 2021. Deep convolutional neural networks for tea tree pest recognition and diagnosis. Symmetry 13:2140

doi: 10.3390/sym13112140
[111]

Lee SH, Lin SR, Chen SF. 2020. Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network. Plant Pathology 69:1731−39

doi: 10.1111/ppa.13251
[112]

Samanta RK, Ghosh I. 2012. Tea insect pests classification based on artificial neural networks. International Journal of Computer Engineering Science 2:1−13

[113]

Yang Z, Feng H, Ruan Y, Weng X. 2023. Tea tree pest detection algorithm based on improved Yolov7-tiny. Agriculture 13:1031

doi: 10.3390/agriculture13051031
[114]

Cui Q, Yang B, Liu B, Li Y, Ning J. 2022. Tea category identification using wavelet signal reconstruction of hyperspectral imagery and machine learning. Agriculture 12:1085

doi: 10.3390/agriculture12081085
[115]

Ning J, Sun J, Li S, Sheng M, Zhang Z. 2017. Classification of five Chinese tea categories with different fermentation degrees using visible and near-infrared hyperspectral imaging. International Journal of Food Properties 20:1515−22

doi: 10.1080/10942912.2016.1233115
[116]

Nidamanuri RR. 2020. Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods. Remote Sensing Applications: Society and Environment 19:100350

doi: 10.1016/j.rsase.2020.100350
[117]

Zhang Z, Yang M, Pan Q, Jin X, Wang G, et al. 2025. Identification of tea plant cultivars based on canopy images using deep learning methods. Scientia Horticulturae 339:113908

doi: 10.1016/j.scienta.2024.113908
[118]

Cui C, Xu Y, Jin G, Zong J, Peng C, et al. 2023. Machine learning applications for identify the geographical origin, variety and processing of black tea using 1H NMR chemical fingerprinting. Food Control 148:109686

doi: 10.1016/j.foodcont.2023.109686
[119]

Liu Y, Huang J, Li M, Chen Y, Cui Q, et al. 2022. Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 267:120537

doi: 10.1016/j.saa.2021.120537
[120]

Hu Y, Kang Z. 2022. The rapid non-destructive detection of adulteration and its degree of Tieguanyin by fluorescence hyperspectral technology. Molecules 27:1196

doi: 10.3390/molecules27041196
[121]

Wei L, Yang Y, Sun D. 2020. Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling. Food Chemistry 329:127177

doi: 10.1016/j.foodchem.2020.127177
[122]

Li L, Jin S, Wang Y, Liu Y, Shen S, et al. 2021. Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 247:119096

doi: 10.1016/j.saa.2020.119096
[123]

Hutter F, Kotthoff L, Vanschoren J. 2019. Automated machine learning: methods, systems, challenges. Cham: Springer. doi: 10.1007/978-3-030-05318-5

[124]

Raschka S, Patterson J, Nolet C. 2020. Machine learning in Python: main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11:193

doi: 10.3390/info11040193
[125]

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. 2018. MobileNetV2: inverted residuals and linear bottlenecks. Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018. pp. 4510−20 doi: 10.1109/CVPR.2018.00474

[126]

Chen H, He G, Peng X, Wang G, Yin R. 2024. A multi-scale feature fusion deep learning network for the extraction of cropland based on landsat data. Remote Sensing 16:4071

doi: 10.3390/rs16214071
[127]

Cang S, Yu H. 2012. Mutual information based input feature selection for classification problems. Decision Support Systems 54:691−98

doi: 10.1016/j.dss.2012.08.014
[128]

Jeon H, Oh S. 2020. Hybrid-recursive feature elimination for efficient feature selection. Applied Sciences 10:3211

doi: 10.3390/app10093211
[129]

Agliari E, Alemanno F, Aquaro M, Fachechi A. 2024. Regularization, early-stopping and dreaming: a hopfield-like setup to address generalization and overfitting. Neural Networks 177:106389

doi: 10.1016/j.neunet.2024.106389
[130]

Ying X. 2019. An overview of overfitting and its solutions. Journal of Physics: Conference Series 1168:022022

doi: 10.1088/1742-6596/1168/2/022022
[131]

Wu J, Chen XY, Zhang H, Xiong LD, Lei H, et al. 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology 17:26−40

doi: 10.11989/JEST.1674-862X.80904120
[132]

Tani L, Rand D, Veelken C, Kadastik M. 2021. Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics. The European Physical Journal C 81:170

doi: 10.1140/epjc/s10052-021-08950-y
[133]

Wang X, Zhu W. 2024. Advances in neural architecture search. National Science Review 11:nwae282

doi: 10.1093/nsr/nwae282
[134]

He Y, Lin J, Liu Z, Wang H, Li LJ, et al. AMC: AutoML for model compression and acceleration on mobile devices. In Computer Vision – ECCV 2018, eds Ferrari V, Hebert M, Sminchisescu C, Weiss Y. Cham: Springer. Vol. 11211. pp. 815–32. doi: 10.1007/978-3-030-01234-2_48

[135]

Li Z, Li Y, Yan C, Yan P, Li X, et al. 2024. Enhancing tea leaf disease identification with lightweight MobileNetV2. Computers, Materials & Continua 80:679−94

doi: 10.32604/cmc.2024.051526
[136]

Wang J, Zareef M, He P, Sun H, Chen Q, et al. 2019. Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms. Journal of the Science of Food and Agriculture 99:5019−27

doi: 10.1002/jsfa.9743
[137]

Sun Y, Wang Y, Huang J, Ren G, Ning J, et al. 2020. Quality assessment of instant green tea using portable NIR spectrometer. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 240:118576

doi: 10.1016/j.saa.2020.118576
[138]

Ding Z, Yang C, Hu B, Guo M, Li J, et al. 2024. Lightweight CNN combined with knowledge distillation for the accurate determination of black tea fermentation degree. Food Research International 194:114929

doi: 10.1016/j.foodres.2024.114929
[139]

Lanjewar MG, Panchbhai KG. 2023. Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud. Neural Computing and Applications 35:2755−71

doi: 10.1007/s00521-022-07743-y
[140]

Zhang G, Chen X, Feng B, Guo X, Hao X, et al. 2022. BCST-APTS: blockchain and CP-ABE empowered data supervision, sharing, and privacy protection scheme for secure and trusted agricultural product traceability system. Security and Communication Networks 2022:2958963

doi: 10.1155/2022/2958963
[141]

Liu Z, Guo J, Yang W, Fan J, Lam KY, et al. 2022. Privacy-preserving aggregation in federated learning: a survey. In IEEE Transactions on Big Data. US: IEEE. pp. 1−20 doi: 10.1109/TBDATA.2022.3190835

[142]

Deng C, Ji X, Rainey C, Zhang J, Lu W. 2020. Integrating machine learning with human knowledge. iScience 23:101656

doi: 10.1016/j.isci.2020.101656
[143]

Bhatt U, Xiang A, Sharma S, Weller A, Taly A, et al. 2020. Explainable machine learning in deployment. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, 2020. New York, NY, USA: Association for Computing Machinery. pp. 648–57 doi: 10.1145/3351095.3375624