"Smart Farming in the Indian Context: Fertilizer Prediction through Ensemble Machine Learning Based Soil Nutrient Analysis"

Abstract

The agricultural landscape in India is undergoing a transformative shift with the integration of advanced technologies to enhance productivity and sustainability. Recognizing the critical role of soil health in agricultural outcomes, this research leverages advanced algorithms to analyze and interpret soil nutrient data, providing farmers with accurate and timely recommendations for optimal fertilizer application. The methodology involves the collection of comprehensive soil nutrient information from various regions, utilizing state-of-the-art sensing technologies. Relationships between soil nutrient levels and crop performance are established through the applying ML models, which include regression & classification methods.  The purpose of this study is to create a fertilizer prediction model capable of anticipating fertilizer requirements based on specific soil characteristics, crop types, and regional variations. The anticipated benefits of this research include improved resource utilization, enhanced crop yields, and reduced environmental impact through the targeted application of fertilizers. By providing farmers with precise recommendations tailored to their specific soil conditions, this approach seeks to contribute to sustainable agricultural practices, economic efficiency, and overall food security in the Indian context. This paper underscores the potential of machine learning applications in revolutionizing traditional farming practices by introducing data-driven decision-making processes.