Seasonal Crop Price Prediction using AI/ML

Abstract: Seasonal crop price prediction plays an important role in modern agriculture by helping farmers, traders, and government agencies make better economic decisions. Traditional forecasting methods often fail to capture complex seasonal patterns, weather variations, and market fluctuations present in agricultural data. This project proposes a crop price prediction system using Long Short-Term Memory (LSTM), a deep learning technique designed for time-series forecasting. The proposed model utilizes historical crop prices along with seasonal, climatic, and market-related factors such as rainfall, temperature, humidity, and previous market trends to predict future crop prices accurately. LSTM networks are highly effective in learning long-term dependencies and sequential patterns in time-series datasets, making them suitable for agricultural price forecasting

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