Forecasting Crop Prices through Machine Learning and Inventory Management

Abstract

Agricultural crop price prediction is a crucial tool for farmers, enabling them to make informed decisionsand optimize their profits. This paper presents the development of a machine learning model designed topredict crop prices, integrating inventory management to enhance accuracy and reliability. The model utilizesa robust dataset that includes variables such as commodity name, state, district, market, minimum price,maximum price, modal price, inventory levels, and date. By employing various machine learning algorithms,we ensure precise predictions tailored to the agricultural context. Additionally, the integration of inventorymanagement practices allows for a better understanding of how stock levels influence market prices,providing deeper insights for farmers. The web application, built using the Flask framework, offers anintuitive interface for users, facilitating easy access to price forecasts and inventory-related insights. A keyfeature of the application is its integrated API, which helps farmers calculate the distance from their locationto the nearest markets, using Google Maps for accurate and real-time measurements. This innovative solutionempowers farmers to make better-informed decisions, ultimately enhancing their economic outcomesthrough improved crop price predictions and effective inventory management.