This is like giving a tea farm a digital “tea master” and a weather-savvy accountant in one: it studies past harvests, weather, and soil data to tell farmers when and how much to pick so they get more tea leaves of better quality with less waste.
Manual decisions about when to harvest, how to manage fields, and how much yield to expect are highly uncertain and depend on expert intuition; this work uses machine learning to predict tea yield and quality, enabling smarter planning, input use, and income stability for tea growers.
Domain-specific agronomic datasets (multi-year tea yield, climate, and soil data) combined with tuned prediction models and local expertise in tea cultivation practices.
Classical-ML (Scikit/XGBoost)
Time-Series DB
Medium (Integration logic)
Data availability and quality across different tea regions (limited, noisy or inconsistent agronomic time-series data), plus need for local recalibration of models to new cultivars, microclimates, and farm practices.
Early Adopters
Focus on tea-specific agronomy and yield/quality prediction rather than generic crop models, leveraging localized climate and soil data to support precision management in a single high-value perennial crop.