Crop Price Prediction Using Machine Learning and Remote Sensing Technology

Abstract

Supply market volatility, climatic variability, and the absence of timely, reliable data for forecasting present serious obstacles for the agriculture industry. the vital role of precise crop price predictions in maintaining global food security and optimising supply chain efficiency. To achieve this, the authors propose a hybrid forecasting model that combines advanced machine learning algorithms with data from remote sensing technologies. By testing this framework across diverse geographic regions and seasons, the study proves that incorporating satellite data significantly enhances the reliability of financial projections. Ultimately, the source highlightshow technological integration provides more robust insights for stakeholders in the agricultural market. This approach aims to provide the predictive accuracy necessary for navigating complex international economic landscapes. The suggested framework is adaptable to various crop types and geographical locations. It offers a decision-support tool for farmers, traders, policymakers, and agribusinesses to make data-driven, informed choices regarding cultivation, storage, and market engagement