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

Author

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  • Emmanuel Ekene Okere

    (Department of Electrical, Electronics and Computer Engineering, Faculty of Engineering & the Built Environment, Cape Peninsula University of Technology, Bellville, Cape Town 7535, South Africa)

  • Vipin Balyan

    (Department of Electrical, Electronics and Computer Engineering, Faculty of Engineering & the Built Environment, Cape Peninsula University of Technology, Bellville, Cape Town 7535, South Africa)

Abstract

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.

Suggested Citation

  • Emmanuel Ekene Okere & Vipin Balyan, 2025. "A Deep Learning-Based Prediction and Forecasting of Tomato Prices for the Cape Town Fresh Produce Market: A Model Comparative Analysis," Forecasting, MDPI, vol. 7(2), pages 1-18, May.
    • Handle: RePEc:gam:jforec:v:7:y:2025:i:2:p:19-:d:1654897
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    References listed on IDEAS

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    1. Helin Yin & Dong Jin & Yeong Hyeon Gu & Chang Jin Park & Sang Keun Han & Seong Joon Yoo, 2020. "STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM," Agriculture, MDPI, vol. 10(12), pages 1-17, December.
    2. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    3. Qi Zhang & Weijia Yang & Anping Zhao & Xiaodong Wang & Zengfei Wang & Lin Zhang, 2024. "Short-term forecasting of vegetable prices based on LSTM model—Evidence from Beijing’s vegetable data," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-33, July.
    4. Chenyun Zhao & Xiaodong Wang & Anping Zhao & Yunpeng Cui & Ting Wang & Juan Liu & Ying Hou & Mo Wang & Li Chen & Huan Li & Jinming Wu & Tan Sun, 2025. "A Vegetable-Price Forecasting Method Based on Mixture of Experts," Agriculture, MDPI, vol. 15(2), pages 1-19, January.
    5. Ikechukwu Kingsley Opara & Olaniyi Amos Fawole & Umezuruike Linus Opara, 2021. "Postharvest Losses of Pomegranate Fruit at the Packhouse and Implications for Sustainability Indicators," Sustainability, MDPI, vol. 13(9), pages 1-19, May.
    6. Feihu Sun & Xianyong Meng & Yan Zhang & Yan Wang & Hongtao Jiang & Pingzeng Liu, 2023. "Agricultural Product Price Forecasting Methods: A Review," Agriculture, MDPI, vol. 13(9), pages 1-20, August.
    7. Baiyegunhi, Lloyd & Sharaunga, Stanley & Dlangis, Sphelele & Ndaba, Nonkhululeko, 2018. "Tomato market integration: a case study of the Durban and Johannesburg fresh produce markets in south Africa," Journal of Agribusiness and Rural Development, University of Life Sciences, Poznan, Poland, vol. 49(3), March.
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