Fertilizer with Crop Prediction Using Machine Learning

Abstract

This paper aims to investigate the potential of machine learning techniques in predicting crop yields and optimizing fertilizer application for enhanced agricultural productivity. The use of accurate crop yield predictions can assist farmers in making informed decisions regarding fertilizer dosage, leading to efficient resource utilization and higher crop yields. In this study, we explore various machine-learning algorithms and analyze their effectiveness in predicting crop yields based on historical data, weather patterns, soil characteristics, and fertilizer application practices. We also propose a framework for optimizing fertilizer dosage using the developed predictive models, aiming to strike a balance between crop yield maximization and minimizing environmental impact. The results of our study demonstrate the promising potential of machine learning in revolutionizing fertilizer management practices, thereby contributing to sustainable agriculture.