Hsin-Cheng ChenShih-Fang ChenShiou-Ruei LinTa-Te Lin2026-01-082026-01-082026-02-01https://scholars.lib.ntu.edu.tw/handle/123456789/735197Timely and precise harvest scheduling is critical for maintaining tea quality and improving labor efficiency. This study aimed to develop an integrated Internet of Things (IoT) and artificial intelligence (AI) framework for automated monitoring and growth modeling of tea shoots, enabling data-driven plantation management. Solar-powered Plantation Monitoring Systems (PMS) were deployed to continuously capture canopy images and environmental data, reducing reliance on manual inspections. An enhanced YOLOv11 segmentation model, incorporating HSI color space conversion, monocular depth estimation, and shape-based temporal tracking, was used to detect pluckable tea shoots with high accuracy. The computed Tea Shoot Density Index (TSDI) showed strong agreement with ground truth measurements (RMSE = 2.542, R2 = 0.931). Three sigmoid growth models − 3PL, 4PL, and Gompertz − were evaluated using growing degree days (GDD) as the time scale. The 4PL model achieved the best performance (RMSE = 0.698, R2 = 0.897) and predicted optimal harvest timing with a mean absolute error (MAE) of 2.7 days, while offering interpretable parameters that reflect shoot retention, growth rate, and maturation dynamics. These parameters provided actionable insights for optimizing irrigation, fertilization, and harvest scheduling across different growth stages. The proposed system delivers a scalable and automated solution for precision tea agriculture, enhancing productivity, improving tea quality, and supporting the transition from experience-based to data-driven management.enTea shootsAutomated monitoringImage segmentationGrowth modelPrecision agriculturejournal article10.1016/j.compag.2025.111251