This review shows that machine learning enables early disease diagnosis, automates selective tea-bud harvesting, evaluates sensory quality using multi-sensor fusion, and predicts yields with higher accuracy than traditional agricultural models. By integrating computer vision, spectroscopy, IoT networks, and lightweight deep-learning architectures, these technologies overcome challenges associated with labor shortages, climate variability, and inconsistent product quality.
Tea is one of the world’s most consumed beverages and a cornerstone of cultural, nutritional, and economic life across more than 60 producing countries. Yet the industry faces persistent difficulties that compromise productivity and sustainability. Climate change amplifies fluctuations in yield and chemical quality, while traditional labor-intensive cultivation struggles with aging rural workforces and rising production costs. Meanwhile, quality varies widely due to complex interactions among cultivar genetics, field management, plucking standards, and post-harvest processing steps. Traditional evaluation systems also rely heavily on subjective human judgment, creating inconsistency in grading and pricing. These longstanding challenges demand innovative approaches capable of analyzing large, multidimensional datasets to support real-time decision making across the entire value chain. Based on these challenges, researchers have conducted an overview of machine-learning-powered innovations to conduct in-depth investigation of the tea industry.
A study (DOI: 10.48130/bpr-0025-0016) published in Beverage Plant Research on 17 October 2025 by Xiaomin Yu’s team, Fujian Agriculture and Forestry University, demonstrates that machine-learning technologies now offer end-to-end solutions that enhance productivity, quality consistency, and sustainability throughout the tea industry.
The review provides an analysis of machine-learning models—from traditional algorithms such as SVM, Random Forests, and K-Means to advanced deep-learning architectures including CNNs, FCNs, RNNs, and autoencoders—and demonstrates how each method supports specific tasks in tea cultivation, harvesting, processing, and quality assurance. In tea quality evaluation, the authors highlight breakthroughs in non-destructive assessment using hyperspectral imaging, near-infrared spectroscopy, electronic nose/tongue systems, and multimodal data fusion, which together achieve accuracy rates exceeding 95% in tea grading and chemical prediction. For cultivation, ML strengthens disease and pest monitoring by integrating segmentation algorithms, attention-enhanced CNNs, and optimized detection frameworks such as YOLO and Faster R-CNN. These systems outperform manual scouting and provide early-warning capability under complex field conditions. In harvesting, ML-enabled computer-vision models now support selective plucking by identifying buds, extracting stem structures, and planning robotic picking sequences with improved precision. Yield prediction and resource optimization benefit from hybrid ML models that combine remote sensing, UAV imagery, meteorological data, and biochemical indicators, achieving more stable predictions than traditional crop models. The review further outlines complete ML pipelines tailored for tea applications, covering data acquisition, preprocessing, feature engineering, model training, evaluation, and deployment. AutoML platforms and lightweight architectures such as MobileNetV2 facilitate deployment on edge devices for real-time field use. Crucially, the review discusses barriers to ML adoption—including inconsistent data quality, limited technical capacity, and integration challenges—and proposes solutions such as domain-specific feature engineering, cloud-based platforms, explainable AI, and industry–academia collaborative frameworks. The authors argue that overcoming these limitations will enable scalable digital transformation across both large estates and smallholder tea farms.
Overall, this review underscores that machine learning has become a transformative force across the tea value chain, offering practical pathways to enhance efficiency, stabilize quality, and improve sustainability. By integrating multimodal sensing, advanced analytics, and intelligent automation, ML equips the tea industry with tools to address climate pressures, labor constraints, and rising market expectations.
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References
DOI
10.48130/bpr-0025-0016
Original Source URL
https://doi.org/10.48130/bpr-0025-0016
Funding information
The research was funded by the State Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops (SKL2023002) and the Fujian Agriculture and Forestry University (FAFU) Construction Project for Technological Innovation and Service System of Tea Industry Chain (K1520005A02).
About Beverage Plant Research
Beverage Plant Research (e-ISSN 2769-2108) is the official journal of Tea Research Institute, Chinese Academy of Agricultural Sciences and China Tea Science Society. Beverage Plant Research is an open-access, online-only journal published by Maximum Academic Press. Beverage Plant Research publishes original research, methods, reviews, editorials, and perspectives that advance the biology, chemistry, processing, and health functions of tea and other important beverage plants.