IoT-Enabled Crop Recommendation in Smart Agriculture Using Machine Learning

Crop recommendation systems driven by IoT data in smart agriculture is a valuable tool in contemporary farming approaches. Such systems increasingly rely on machine learning techniques to reason over the most suitable crops according to soil, environmental and weather parameters continously mea-sured by IoT sensors. This paper applies a set of state-of-the-art machine learning models for crop recommendation using an open dataset for multi-class classification. Evaluation results show that Random Forest classifier outperforms all the other models that were employed in our research.