Survey on Crop Prediction using Machine Learning

Abstract

Agriculture, a vital sector for sustaining global food security, faces significant challenges due to the growing population and environmental changes. Traditional practices often struggle with inefficiencies and adaptability issues. This survey examines recent advancements in crop prediction through the integration of machine learning (ML) and other technologies. It highlights how ML algorithms analyze diverse datasets, including satellite imagery and soil data, to enhance prediction accuracy and optimize resource use. The review underscores the impact of these technologies on improving decision-making for farmers, stabilizing food prices, and
promoting agricultural sustainability.