AI-Driven Agricultural Commodity Price Forecasting with Hybrid Models and Scalable Data Infrastructure

January 1, 2026Open Access

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Key Points

The aim is to study and develop machine learning methodologies for forecasting agricultural commodity prices, focusing on grains.
Conducted a systematic review of international literature on forecasting techniques in agriculture.
Designed a scalable data warehouse architecture using big data technologies to manage large volumes of agricultural data.
Compared different forecasting algorithms for accuracy, scalability, and generalization ability on real-time data.
Demonstrated the effectiveness of advanced machine learning techniques in improving agricultural price forecasting.
Highlight the importance of scalable data infrastructure for decision support in agriculture.

Examines AI methodologies for predicting agricultural prices, suggesting improved economic analysis.

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Αστέριος Α. Θεοφίλου (2026) studied this question.

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