TeaGuard: Advanced Detection for Leaf Health and Optimal Harvest Timing

Abstract

Tea planters face tremendous challenges in the identification of leaf diseases and determination of the optimal harvesting time. Manually applied inspection methods are often ineffective, inconsistent, and subject to human mistakes, leading to reduced yield quality and economic loss. While machine learning has been increasingly popular in agriculture, the majority of the existing models only tackle disease detection or ripeness assessment separately. This lack of a unified solution brings inefficiencies, resulting in farmers utilizing many systems. Additionally, high costs of computations and inability to generalize for multiple teas further limit the utilization of such technologies in the field. To address these challenges, the present paper proposes TeaGuard, a Progressive Web Application (PWA) that utilizes EfficientNetB3 and transfer learning for real-time tea leaf disease classification and tea ripeness assessment. The system captures tea leaf images using preprocessing techniques such as image resizing and normalization to improve model performance. High-speed communication between the frontend and machine learning models is supported by the FastAPI backend with the best performance in real-time inference. The model employs a lightweight but efficient architecture with high accuracy at the cost of efficiency in deployment in low-resource environments. The model for classifying diseases achieved 94% accuracy, which presents very good classification performance on diverse categories of disease. The ripeness estimation model now achieves 84% accuracy, which is a significant advancement in predicting ideal harvest times. The system was validated through functional, non-functional, and model tests, which confirmed the robustness, scalability, and feasibility of the system for deployment.