An AI-Enabled Crop, Fertilizer, And Yield Recommendation System Using Machine Learning

Abstract: Agriculture plays a vital role in global food security, and farmers constantly seek ways to optimize crop selection to maximize yield and profitability. However, identifying the most suitable crop for a specific region is challenging due to factors such as climate conditions, soil fertility, rainfall, temperature, and water availability. Traditional farming practices often rely on experience and assumptions, which may lead to reduced productivity and improper fertilizer usage.The proposed Crop and Fertilizer Recommendation System addresses these challenges by utilizing machine learning techniques to analyze environmental and soil-related parameters for intelligent decision- making. The system collects input data such as soil nutrients, temperature, humidity, rainfall, and pH values, and processes them using machine learning algorithms to recommend the most suitable crop and appropriate fertilizer. By providing accurate and data-driven recommendations, the system helps farmers improve crop yield, reduce resource wastage, and enhance sustainable agricultural practices.The developed model aims to support precision agriculture by assisting farmers in selecting crops that are best suited for their land conditions while also suggesting fertilizers to maintain soil health and productivity. Experimental results demonstrate that machine learning-based recommendations can significantly improve agricultural efficiency and contribute to smarter farming solutions.