Voting Based Ensemble Framework For Crop Recommendation

Agriculture holds paramount importance for a significant portion of India’s population, contributing significantly to the national economy. However, many Indian farmers rely on traditional senses rather than data-driven insights for crop selection, risking negative impacts on their mental and financial well-being. This article emphasizes the potential of Machine Learning models, utilizing a comprehensive dataset combining factors like rainfall, climate, humidity, and soil nutrients, to guide farmers in making informed decisions for optimal crop selection. To address the challenges faced by Indian farmers, the proposed methodology involves the application of Machine Learning models to a dataset encompassing crucial parameters such as sowing season, soil properties, climate, and location. By leveraging advanced algorithms, the models aim to recommend crops tailored to the farm’s specific conditions, ensuring precision and accuracy in decision-making. This data-driven approach seeks to revolutionize traditional farming practices and enhance overall agricultural outcomes. The application of Machine Learning models on a comprehensive dataset demonstrated a robust recommendation system for crop selection, considering factors like soil health and weather conditions. This technology-driven approach empowers Indian farmers with informed decision-making, mitigating risks and fostering sustainable agricultural practices.