A Review on Fertilizer Recommendation System

Our project introduces a Fertilizer Recommendation System (FRS) using advanced Machine Learning (ML) techniques to help farmers maximize crop yield while minimizing the environmental impact of fertilizer use. Analyzing soil data stored in a database generates personalized fertilizer recommendations based on specific soil conditions and historical crop performance. It employs various ML models like decision trees and neural networks, enhanced through feature engineering, to establish patterns linking soil properties to nutrient requirements. Soil sensors fetch real-time data, enabling accurate recommendations. The evaluation shows our ML-based FRS outperforms traditional methods, promising improved agricultural productivity and ecological sustainability by guiding farmers in efficient nutrient management for optimal crop growth and environmental conservation.