IoT-Enabled Crop Recommendation System

Abstract: Precision agriculture has become increasingly important in modern farming practices, aiming to optimize crop yields while minimizing resource use and environmental impact. In this study, we propose an IoT-based crop recommendation system utilizing the Random Forest algorithm to assist farmers in making informed decisions about crop selection based on real-time environmental data. The system leverages IoT sensors to continuously monitor key factors such as pH, temperature, nitrogen, phosphorus, and rainfall in the field. These data are preprocessed and used to train the Random Forest model, which learns the complex relationships between environmental conditions and optimal crop choices. The trained model provides timely recommendations to farmers, helping them adapt to changing conditions and maximize productivity. Through continuous feedback and retraining, the system aims to improve recommendation accuracy over time. This approach holds promise for enhancing agricultural sustainability and efficiency in modern farming practices.

Keywords: IoT, Machine learning, Predictive Analysis, Resource Optimization, Smart Farming