Forecasting Agricultural Yield and Market Price using Time Series Models

Agriculture is a cornerstone of global food security, but it is vulnerable to unpredictable environmental conditions and fluctuating market prices. This project focuses on developing an advanced system for forecasting agricultural yields and market prices using machine learning algorithms. By integrating data from various sources, such as historical weather patterns, soil quality metrics, and crop production records, the system aims to provide accurate predictions. These insights will assist farmers and policymakers in optimizing agricultural practices and making informed decisions, ultimately enhancing productiv- ity and economic stability. The project contributes to the growing field of agricultural technology, addressing challenges related to food security and sustainable farming practices in the face of environmental and market uncertainties. Index Terms—Agricultural Forecasting, Machine Learning, Crop Yield Prediction, Market Price Prediction, Data Integration, Predictive Modeling, Sustainability