Leveraging Geospatial Data and Machine Learning to Improve Regional Crop Price Forecasts

Abstract

For farmers, merchants, legislators, and other players in the agri-supply chain, accurate crop price forecasting is essential to lowering market uncertainty and enhancing income stability. However, due to significant spatial variability, seasonal production patterns, climatic shocks, and poor market integration across several geographic zones, regional crop price prediction is still difficult. In order to enhance regional crop price estimates, this study suggests an integrated strategy that makes use of machine learning (ML) and geospatial data. The study makes use of a variety of sources, such as historical market pricing data from agricultural marketing systems, satellite-derived vegetation indicators like the Normalized Difference Vegetation Index (NDVI), and meteorological parameters like temperature and rainfall. Data cleaning, dataset spatial alignment, feature extraction (NDVI trends, rainfall anomalies, and distance-based market connectedness), and predictive modeling utilizing machine learning techniques like Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks are all part of the methodology. Error-based metrics such as MAE, RMSE, and MAPE are used to evaluate model performance, while feature importance and comparative model testing with and without geographical variables are used to analyze the role of geospatial predictors. The anticipated result is a forecasting system that incorporates spatial crop health and weather data to show enhanced robustness in regional price prediction. The study comes to the conclusion that using geographical information in conjunction with machine learning can improve crop price forecasting systems' interpretability and utility for local decision-making, allowing for more proactive market planning and risk reduction.