AI-Driven Tomato Crop Management System with Collaborative Learning

Tomato cultivation in India faces persistent chal-lenges due to unpredictable weather patterns, soil variability, pest outbreaks, and inefficient post-harvest practices. To address these issues, this project proposes an integrated, AI-driven tomato crop management system that leverages energy-efficient IoT devices, computer vision, and federated learning to assist farmers with real-time, actionable insights. The system employs ESP32 microcontrollers connected to soil, pH, temperature, and light sensors, along with a vision-based assessment using CNN models deployed via Roboflow for disease detection and fruit ripeness evaluation. A user-friendly dashboard built with Vanilla JavaScript provides alerts, treatment suggestions, and crop health recommendations. Key innovations include solar-powered operation, low-cost implementation, and decentralized learning models that adapt to regional farming conditions while preserving data privacy. Field testing confirmed that the sys-tem accurately monitors environmental parameters, classifies diseases, and enhances decision-making for improved crop yield and resource efficiency. The results validate the practicality and scalability of the system for smallholder farmers across diverse agro-climatic zones.