Abstract: Sustainable agriculture requires efficient fertilizer management to enhance crop yield while preserving soil health and the environment. This project presents a Sustainable Fertilizer Usage Optimizer that leverages machine learning and precision farming techniques to recommend optimal fertilizer usage based on soil nutrients, crop type, and environmental conditions. The system integrates crop recommendation, yield prediction, and fertilizer optimization into a unified web-based platform developed using Flask. By minimizing excessive fertilizer application and promoting balanced nutrient management, the proposed solution improves productivity, reduces costs, and supports eco-friendly farming practices. The system empowers farmers with data-driven insights for sustainable and profitable agriculture.
Keywords: Sustainable Agriculture, Fertilizer Optimization, Crop Yield Prediction, Precision Farming, Machine Learning, Soil Nutrient Management, Smart Agriculture, Web-Based Decision Support System.
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DOI:
10.17148/IJARCCE.2025.1412112
[1] Dr.Sapna B Kulkarni, K Anil Kumar, A Pavan Kumar Reddy, Nithin Yadav G, Bharath G, "Sustainable fertilizer usage optimizer for higher yield," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.1412112
