ArecaSafe: An Intelligent Sentinel for Arecanut Crop Health

ArecaSafe is an intelligent framework powered by the Internet of Things (IoT) and Artificial Intelligence (AI) that is designed to help monitor Arecanut (Areca catechu) plantations ahead of time and find Fruit Rot disease early. The system uses real-time environmental monitoring, machine learning to anticipate climate risk, and deep learning to analyze images to give accurate and rapid illness assessments. The climate prediction module uses a Random Forest (RF) classifier that has been trained on 10 years' worth of weather data (2014–2024) from the Zonal Agricultural and Horticultural Research Station (ZAHRS) in Brahmavara. The image-based illness detection system also uses a Convolutional Neural Network (CNN) driven by EfficientNetB0 and transfer learning to tell the difference between healthy and unhealthy arecanut fruits. An ESP32-based IoT node with BME280 (temperature and humidity), BH1750 (light intensity), and rainfall sensors collects environmental data. This data is then sent through a FastAPI–Supabase cloud infrastructure. Experimental results reveal that the system can properly classify several levels of disease severity—Low, Moderate, and High—and also performs well in identifying visual symptoms through picture analysis. In general, ArecaSafe is a useful, cheap, and data-driven solution for precision agriculture. It helps farmers take prompt steps to prevent crop loss and keep yields stable over the long run.