Seasonal Crop Price Prediction using LSTM

Abstract: Predicting agricultural crop prices is a critical task that helps farmers and agricultural stakeholders make informed decisions regarding cultivation, storage, and marketing strategies. Crop prices are influenced by various factors, including seasonal changes, weather conditions, market demand, and supply fluctuations. Due to the complex and dynamic nature of these factors, accurate forecasting remains a significant challenge. This project proposes a Seasonal Crop Price Prediction system using Machine Learning and Long Short-Term Memory (LSTM) networks.

The system analyzes historical crop price data along with environmental and market-related parameters such as temperature, rainfall, humidity, production levels, and previous price trends. LSTM, a powerful recurrent neural network architecture, is capable of capturing temporal patterns and long-term dependencies within time-series data, enabling more precise price predictions. The developed model assists farmers, traders, and policymakers by providing early insights into future market prices, helping reduce risks and improve planning. By leveraging advanced deep learning techniques, the proposed solution contributes to the development of data-driven and sustainable agricultural practices

Keywords: Seasonal Crop Price Prediction, LSTM, Machine Learning, Deep Learning, Time-Series Forecasting, Smart Agriculture, Agricultural Data Analysis.