Agriculture Crop Price Forecasting and Advisory System

Agriculture plays a vital role in economic development, yet farmers face significant challenges due to unpredictable fluctuations in crop prices caused by weather conditions, market demand, and supply variations. These uncertainties often result in financial losses and inefficient decision-making. This paper presents an Agriculture Crop Price Forecasting and Advisory System that utilizes machine learning and data analytics techniques to predict future crop prices and provide actionable recommendations. The system analyzes historical agricultural data, including rainfall, temperature, seasonal trends, and market conditions, to identify patterns influencing price variations. A Linear Regression model is employed to forecast crop prices, and its performance is evaluated using statistical measures such as Mean Squared Error and R-squared score. Additionally, an interactive dashboard is developed using Power BI to visualize insights and trends effectively. The proposed system enables farmers to make informed decisions regarding crop selection and selling time, thereby improving profitability and reducing risk. The results demonstrate that the system provides reliable predictions and supports data-driven agricultural practices.