SUSTAINABLE FERTILIZER USAGE OPTIMIZER FOR HIGHER YIELD

Abstract: Agriculture is an important part of our daily life, but farmers face many problems such as increasing fertilizer costs, poor soil quality, changing weather conditions, and improper use of fertilizers. Using too much fertilizer can damage the soil and cause water pollution, while using less fertilizer than required can reduce crop growth and yield. Therefore, there is a need for a smart system that can help farmers use fertilizers in the correct amount.

This research proposes a Sustainable Fertilizer Usage Optimizer for Higher Yield using Data Analytics and Machine Learning. The system uses information such as soil nutrients (Nitrogen, Phosphorus and Potassium), soil pH, crop type, weather conditions, previous crop yield, and fertilizer usage. The collected data is cleaned and analyzed before applying suitable machine learning algorithms such as Random Forest and regression models. The system then recommends a suitable fertilizer and quantity according to the crop and soil conditions. The expected outcome is to reduce unnecessary fertilizer usage, decrease farming costs, maintain soil health, and improve crop yield. Overall, the proposed system aims to support smart, economical, and environmentally friendly farming.

Keywords: Sustainable Agriculture, Fertilizer Optimization, Machine Learning, Data Analytics, Random Forest, Crop Yield Prediction, Soil Nutrients, NPK, Precision Agriculture, Fertilizer Recommendation.