A Hybrid IoT and Machine Learning Approach for Crop Recommendation Using a Voting Ensemble Model

This project introduces a hybrid IoT and machine learning approach for crop recommendation using a Voting Ensemble Model. The system employs IoT sensors to monitor essential parameters such as soil moisture, temperature, humidity, light intensity, and pH levels. This data is collected and processed to determine whether current conditions are suitable ("good") or unsuitable ("bad") for optimal crop growth. The Voting Ensemble Model combines predictions from multiple machine learning algorithms to enhance accuracy and reliability. Real-time recommendations and alerts are provided to farmers, enabling timely interventions to improve crop yield and management. This integrated approach ensures effective decision-making and supports sustainable agricultural practices by leveraging advanced data analytics and IoT technology.

Keywords: improve crop yield, IOT, GSM module