Agriculture Commodity Price Forecasting

Abstract

Agriculture commodity price forecasting is a critical task in the agricultural and economic sectors, as accurate price prediction helps farmers, traders, wholesalers, and policymakers make informed decisions regarding production planning, storage, transportation, and market strategies. Agricultural commodity prices are highly volatile due to multiple influencing factors such as weather conditions, seasonal demand, crop yield variations, market trends, government policies, inflation, and supply chain disruptions. Traditional statistical forecasting methods often fail to capture the complex nonlinear relationships and temporal dependencies present in agricultural market data, resulting in lower prediction accuracy. To address these limitations, this study proposes an advanced forecasting framework based on Hybrid Enhanced Long Short-Term Memory (HE-LSTM) for accurate agricultural commodity price prediction. The proposed HE-LSTM model combines the strengths of deep learning sequence modeling with enhanced feature learning mechanisms to effectively capture long-term dependencies and hidden patterns in historical commodity price datasets. The system utilizes historical market price records along with relevant influencing attributes such as commodity type, date-wise trends, seasonal patterns, and external economic indicators to improve forecasting performance. Data preprocessing techniques including missing value handling, normalization, feature engineering, and outlier removal are applied to enhance data quality and ensure robust model training. The HE-LSTM architecture is designed to overcome the limitations of conventional LSTM models by improving sequence learning efficiency, reducing prediction error, and enhancing generalization capability for highly fluctuating agricultural market conditions. Experimental analysis demonstrates that the proposed model achieves superior forecasting accuracy compared with traditional machine learning models and baseline deep learning techniques based on evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy. The developed forecasting system provides a scalable, intelligent, and practical solution for agricultural commodity price analysis, enabling better market decision-making, risk reduction, and improved financial planning for stakeholders in the agricultural ecosystem.

Introduction

Agriculture is a vital sector that supports livelihoods and national economies, but agricultural commodity prices are highly volatile due to factors such as weather conditions, seasonal demand, supply fluctuations, transportation costs, government policies, and economic changes. These unpredictable price variations make it difficult for farmers, traders, wholesalers, and policymakers to make informed decisions regarding production, storage, and sales. Traditional forecasting methods, including statistical models and basic machine learning techniques, often fail to capture the complex nonlinear relationships and long-term patterns present in agricultural market data, leading to inaccurate predictions.

To address these challenges, the proposed system uses a Hybrid Enhanced Long Short-Term Memory (HE-LSTM) model for agricultural commodity price forecasting. HE-LSTM combines advanced deep learning capabilities with enhanced feature learning techniques to improve forecasting accuracy and robustness. The system utilizes historical commodity prices, seasonal trends, market indicators, and other influencing factors to predict future prices more effectively.

The framework consists of several modules, including data collection, preprocessing, HE-LSTM model training, price prediction, performance evaluation, and decision support. Data preprocessing involves cleaning, normalization, feature engineering, and handling missing values to improve model performance. The trained HE-LSTM model learns hidden temporal patterns and complex market relationships, enabling more accurate future price predictions. Model performance is evaluated using metrics such as MAE, RMSE, and prediction accuracy.

The proposed system offers an automated, scalable, and intelligent forecasting solution that helps farmers determine optimal selling times, assists traders in reducing financial risks, and supports policymakers in making informed market decisions. Future enhancements include integrating real-time market data, weather and environmental factors, advanced deep learning architectures such as Transformers, mobile and web applications, visualization dashboards, multilingual support, and recommendation systems for storage and selling strategies. Overall, the HE-LSTM-based forecasting system aims to improve agricultural market planning, profitability, and decision-making through accurate and reliable price predictions.

Conclusion

The proposed Agriculture Commodity Price Forecasting using HE-LSTM system provides an intelligent and efficient solution for predicting future agricultural commodity prices with improved accuracy and reliability. Agricultural markets are highly dynamic and influenced by various factors such as seasonal demand, weather conditions, supply fluctuations, and economic changes, making accurate forecasting a challenging task. The implementation of the Hybrid Enhanced Long Short-Term Memory (HE-LSTM) model helps overcome the limitations of traditional statistical and basic machine learning methods by effectively capturing complex nonlinear relationships and long-term temporal dependencies in historical market data. Through data preprocessing, advanced sequence learning, and predictive analysis, the proposed system generates accurate commodity price forecasts that support better decision-making for farmers, traders, wholesalers, and policymakers. The evaluation of the model using standard performance metrics demonstrates its effectiveness in reducing forecasting errors and improving prediction performance. Overall, the developed system offers a scalable, automated, and practical forecasting framework that contributes to smarter agricultural market planning, reduced financial risks, and improved profitability in the agricultural sector.

Copyright

Copyright © 2026 Abisheik K, Tamilarasan M, Mithun Chakkaravarthi N N, Senthil Murugan N. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.