Machine Learning Approaches for Crop Prediction and Fertilizer Recommendation based on Soil Nutrients.

Abstract

This paper explores the application of machine learning (ML) techniques to improve crop prediction and fertilizer recommendation based on soil nutrient data. The primary objective is to enhance agricultural productivity while promoting sustainable resource management. Through an in-depth review of ML models—such as regression, classification, and deep learning—this study identifies effective methods for matching crop types to specific soil conditions and optimizing fertilizer application. Key methods include supervised learning for yield prediction, reinforcement learning for adaptive nutrient recommendations, and IoT integration for real-time data analysis. Findings highlight the potential of ML to transform traditional agriculture by offering high-precision, data-driven insights that reduce resource wastage and environmental impact. Future research is suggested to address data challenges, model interpretability, and scalability, aiming to make ML tools more accessible and impactful for diverse agricultural contexts.