NEXT-GEN CROP MANAGEMENT AND OPTIMIZATION PLATFORM

Abstract

This project proposes a machine learning–based model to optimize agricultural practices by selecting the most suitable crop for cultivation, predicting its yield, and providing fertilizer recommendations based on weather parameters and soil characteristics. The model employs a hybrid approach that combines the strengths of Random Forest and Logistic Regression algorithms to achieve high accuracy. Compared to existing systems, this solution introduces several advantages, as it accurately identifies the most suitable crop based on multiple factors to maximize yield potential, utilizes weather and soil data to forecast crop yield with greater precision, and provides personalized fertilizer suggestions tailored to the predicted crop and soil conditions. In addition, it offers a user-friendly interface that enables farmers to easily input data and receive actionable insights for better decision-making. This abstract outlines a groundbreaking platform for next-generation crop management and optimization. At its core is an innovative machine learning model designed to revolutionize agricultural decisionmaking by enabling farmers to make informed choices regarding crop selection, yield estimation, and fertilizer application. By leveraging a hybrid approach that combines Random Forest and Logistic Regression, the system delivers highly accurate predictions and recommendations, paving the way for sustainable and efficient agricultural practices.