A Deep Learning-Based Prediction and Forecasting of Tomato Prices for the Cape Town Fresh Produce Market: A Model Comparative Analysis

• Published in: Forecasting Vol. 7; no. 2; p. 19
• Main Authors: Okere, Emmanuel Ekene, Balyan, Vipin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Agricultural commodities; Algorithms; Commodity price indexes; Commodity prices; Comparative analysis; Deep learning; Econometrics; Economic analysis; Food waste; Forecasting; LSTM; Machine learning; Neural networks; Performance evaluation; prediction; Prediction theory; Prices and rates; Pricing; Root-mean-square errors; Supply and demand; Supply chains; Time series; time series analysis; Tomatoes; Vegetable industry; Vegetables; South Africa
• ISSN: 2571-9394; 2571-9394
• DOI: 10.3390/forecast7020019

The fresh produce supply chain sector is a vital pillar of any society and an indispensable part of the national economic structure. As a significant segment of the agricultural market, accurately forecasting vegetable prices holds significant importance. Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode. Future planning for commodity pricing is achievable by forecasting the future price anticipated by the current circumstances. This paper presents a price forecasting methodology for tomatoes which uses price and production data taken from 2008 to 2021 and analyzed by means of advanced deep learning-based Long Short-Term Memory (LSTM) networks. A comparative analysis of three models based on Root Mean Square Error (RMSE) identifies LSTM as the most accurate model, achieving the lowest RMSE (0.2818), while SARIMA performs relatively well. The proposed deep learning-based method significantly improved the results versus other conventional machine learning and statistical time series analysis methods.