Predictive Analysis of Market trends in Agriculture using ML/AI Techniques

Abstract

The paper aims to predict crop prices to create a system that recommends crop choices, reduces price-related risks, and suggests optimal planting times. This paper approaches the problem of predicting crop prices by employing Time Series Fore-casting with a focus on ARIMA modeling. Our main objectives are to cut down on risks associated with price fluctuations and provide practical decision support for farmers, aiding in optimal crop choices and planting times. Starting with a detailed exploration of agricultural trends, we identify key factors influencing crop prices. We then dive into the application of advanced Time Series Forecasting techniques, specifically ARIMA modeling, to analyze historical monthly data for accurate predictions. The reliability of our models is ensured through thorough checks for stationarity. The paper emphasizes the crucial role of precise price forecasts in effective risk management for farmers. By integrating these insights into decision-making processes, farmers gain the ability to navigate market uncertainties and optimize their agricultural practices.