A Hydrid IOT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model

This paper presents a hybrid Internet of Things (IoT) and Machine Learning (ML) framework for intelligent crop recommendation, leveraging ensemble-based predictive modeling. Agricultural productivity in developing regions is often hindered by poor soil management, unpredictable weather, and lack of timely crop guidance. The proposed system integrates IoT-enabled environmental monitoring with a voting ensemble model that combines the predictive strengths of multiple algorithms, including Random Forest, Support Vector Machine (SVM), and Gradient Boosting. Real-time data such as soil moisture, pH, temperature, humidity, and rainfall are collected through IoT sensors and stored in a cloud-based platform for processing. The ensemble model dynamically recommends the most suitable crop for the current environmental conditions, enhancing decision accuracy and farm efficiency. Experimental results demonstrate that the hybrid model achieves improved precision, recall, and F1-score compared to individual models, indicating strong potential for sustainable smart farming applications.