| [1] |
Yu X, Xiao J, Chen S, Yu Y, Ma J, et al. 2020. Metabolite signatures of diverse Camellia sinensis tea populations. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [7] |
Shen J, Wang Y, Chen C, Ding Z, Hu J, et al. 2015. Metabolite profiling of tea (Camellia sinensis L.) leaves in winter. |
| [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. |
| [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. |
| [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. |
| [11] |
Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. 2018. Machine learning in agriculture: a review. |
| [12] |
Kasinathan T, Singaraju D, Uyyala SR. 2021. Insect classification and detection in field crops using modern machine learning techniques. |
| [13] |
Sujatha R, Chatterjee JM, Jhanjhi N, Brohi SN. 2021. Performance of deep learning vs machine learning in plant leaf disease detection. |
| [14] |
van Klompenburg T, Kassahun A, Catal C. 2020. Crop yield prediction using machine learning: a systematic literature review. |
| [15] |
Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, et al. 2021. Machine learning in agriculture: a comprehensive updated review. |
| [16] |
Hamrani A, Akbarzadeh A, Madramootoo CA. 2020. Machine learning for predicting greenhouse gas emissions from agricultural soils. |
| [17] |
Wang H, Gu J, Wang M. 2023. A review on the application of computer vision and machine learning in the tea industry. |
| [18] |
Xu Q, Zhou Y, Wu L. 2024. Advancing tea detection with artificial intelligence: strategies, progress, and future prospects. |
| [19] |
Wei Y, Wen Y, Huang X, Ma P, Wang L, et al. 2024. The dawn of intelligent technologies in tea industry. |
| [20] |
Wang P, Fan E, Wang P. 2021. Comparative analysis of image classification algorithms based on traditional machine learning and deep learning. |
| [21] |
Wang J, Ma Y, Zhang L, Gao RX, Wu D. 2018. Deep learning for smart manufacturing: methods and applications. |
| [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. |
| [23] |
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. 2020. Introduction to machine learning, neural networks, and deep learning. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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). |
| [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. |
| [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. |
| [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. |
| [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). |
| [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. |
| [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. |
| [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. |
| [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. |
| [42] |
Wu X, Yang J, Wang S. 2018. Tea category identification based on optimal wavelet entropy and weighted k-Nearest Neighbors algorithm. |
| [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. |
| [44] |
Xu M, Wang J, Zhu L. 2021. Tea quality evaluation by applying E-nose combined with chemometrics methods. |
| [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. |
| [46] |
Shao P, Wu M, Wang X, Zhou J, Liu S. 2018. Research on the tea bud recognition based on improved k-means algorithm. |
| [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. |
| [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. |
| [50] |
Kondratovich E, Baskin II, Varnek A. 2013. Transductive support vector machines: promising approach to model small and unbalanced datasets. |
| [51] |
Yang J, Chen Y. 2022. Tender leaf identification for early-spring green tea based on semi-supervised learning and image processing. |
| [52] |
Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, et al. 2015. Human-level control through deep reinforcement learning. |
| [53] |
DeepSeek-AI, Guo D, Yang D, Zhang H, Song J, et al. 2025. DeepSeek-R1: incentivizing reasoning capability in LLMs via reinforcement learning. |
| [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. |
| [55] |
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. |
| [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. |
| [59] |
Shi M, Zheng D, Wu T, Zhang W, Fu R, et al. 2024. Small object detection algorithm incorporating swin transformer for tea buds. |
| [60] |
Xue Z, Xu R, Bai D, Lin H. 2023. YOLO-tea: a tea disease detection model improved by YOLOv5. |
| [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. |
| [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. |
| [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. |
| [64] |
Chen YT, Chen SF. 2020. Localizing plucking points of tea leaves using deep convolutional neural networks. |
| [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. |
| [66] |
Zhu N, Liu X, Liu Z, Hu K, Wang Y, et al. 2018. Deep learning for smart agriculture: concepts, tools, applications, and opportunities. |
| [67] |
Yao Z, Zhu X, Zeng Y, Qiu X. 2023. Extracting tea plantations from multitemporal Sentinel-2 images based on deep learning networks. |
| [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. |
| [69] |
Li H, Mao Y, Wang Y, Fan K, Shi H, et al. 2022. Environmental simulation model for rapid prediction of tea seedling growth. |
| [70] |
Huang Y, Jiang H, Wang W. 2022. Research on tea tree growth monitoring model using soil information. |
| [71] |
Chen X, Hassan MM, Yu J, Zhu A, Han Z, et al. 2024. Time series prediction of insect pests in tea gardens. |
| [72] |
Krishnan Jayapal S, Poruran S. 2023. Enhanced disease identification model for tea plant using deep learning. |
| [73] |
Zhang J, Guo H, Guo J, Zhang J. 2023. An information entropy masked vision transformer (IEM-ViT) model for recognition of tea diseases. |
| [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. |
| [75] |
Cimpoiu C, Cristea VM, Hosu A, Sandru M, Seserman L. 2011. Antioxidant activity prediction and classification of some teas using artificial neural networks. |
| [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. |
| [77] |
Chen Q, Zhao J, Vittayapadung S. 2008. Identification of the green tea grade level using electronic tongue and pattern recognition. |
| [78] |
Li X, He Y. 2008. Discriminating varieties of tea plant based on Vis/NIR spectral characteristics and using artificial neural networks. |
| [79] |
Mienye ID, Sun Y. 2022. A survey of ensemble learning: concepts, algorithms, applications, and prospects. |
| [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. |
| [81] |
Raza A, Hu Y, Lu Y. 2024. Improving carbon flux estimation in tea plantation ecosystems: a machine learning ensemble approach. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [87] |
Ren G, Wang Y, Ning J, Zhang Z. 2021. Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology. |
| [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. |
| [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. |
| [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. |
| [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. |
| [94] |
Wang T, Zhang K, Zhang W, Wang R, Wan S, et al. 2023. Tea picking point detection and location based on Mask-RCNN. |
| [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. |
| [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. |
| [97] |
Xu H, Ma W, Tan Y, Liu X, Zheng Y, et al. 2022. Yield estimation method for tea based on YOLOv5 deep learning. |
| [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. |
| [99] |
Huang Y. 2023. Improved SVM-based soil-moisture-content prediction model for tea plantation. |
| [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. |
| [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. |
| [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. |
| [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. |
| [104] |
Chen J, Liu Q, Gao L. 2019. Visual tea leaf disease recognition using a convolutional neural network model. |
| [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. |
| [106] |
Sun Y, Jiang Z, Zhang L, Dong W, Rao Y. 2019. SLIC_SVM based leaf diseases saliency map extraction of tea plant. |
| [107] |
Hu G, Wang H, Zhang Y, Wan M. 2021. Detection and severity analysis of tea leaf blight based on deep learning. |
| [108] |
Hu G, Wei K, Zhang Y, Bao W, Liang D. 2021. Estimation of tea leaf blight severity in natural scene images. |
| [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. |
| [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. |
| [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. |
| [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. |
| [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. |
| [116] |
Nidamanuri RR. 2020. Hyperspectral discrimination of tea plant varieties using machine learning, and spectral matching methods. |
| [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. |
| [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. |
| [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. |
| [120] |
Hu Y, Kang Z. 2022. The rapid non-destructive detection of adulteration and its degree of Tieguanyin by fluorescence hyperspectral technology. |
| [121] |
Wei L, Yang Y, Sun D. 2020. Rapid detection of carmine in black tea with spectrophotometry coupled predictive modelling. |
| [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. |
| [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. |
| [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. |
| [127] |
Cang S, Yu H. 2012. Mutual information based input feature selection for classification problems. |
| [128] |
Jeon H, Oh S. 2020. Hybrid-recursive feature elimination for efficient feature selection. |
| [129] |
Agliari E, Alemanno F, Aquaro M, Fachechi A. 2024. Regularization, early-stopping and dreaming: a hopfield-like setup to address generalization and overfitting. |
| [130] |
Ying X. 2019. An overview of overfitting and its solutions. |
| [131] |
Wu J, Chen XY, Zhang H, Xiong LD, Lei H, et al. 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. |
| [132] |
Tani L, Rand D, Veelken C, Kadastik M. 2021. Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics. |
| [133] |
Wang X, Zhu W. 2024. Advances in neural architecture search. |
| [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. |
| [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. |
| [137] |
Sun Y, Wang Y, Huang J, Ren G, Ning J, et al. 2020. Quality assessment of instant green tea using portable NIR spectrometer. |
| [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. |
| [139] |
Lanjewar MG, Panchbhai KG. 2023. Convolutional neural network based tea leaf disease prediction system on smart phone using paas cloud. |
| [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. |
| [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. |
| [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 |