Price Prediction of Agricultural Products: Machine Learning

Rino Cerna; Eduardo Tirado; Sussy Bayona-Oré

doi:10.1007/978-981-16-2102-4_78

Price Prediction of Agricultural Products: Machine Learning

Rino Cerna, Eduardo Tirado, Sussy Bayona-Oré

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Family farming is essentially characterized by the use of family labor force, due to the lack of land, water, and capital resources. An important tool is which allows them to know which products will be the best priced when production is completed, and at this point machine learning technology has, in particular, models and algorithms that allow for price prediction. The aim of this work is to review the literature related to price prediction of agricultural products using machine learning technology with the purpose of identifying the prediction models used in the studies. It also aims to identify the agricultural products used in these predictions to discuss their application in other products. The results show that neural network model is the most used in the selected studies.

Original language	English
Title of host publication	Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	879-887
Number of pages	9
ISBN (Print)	9789811621017
DOIs	https://doi.org/10.1007/978-981-16-2102-4_78
State	Published - 2022
Externally published	Yes
Event	6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online Duration: 25 Feb 2021 → 26 Feb 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	217
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	6th International Congress on Information and Communication Technology, ICICT 2021
City	Virtual, Online
Period	25/02/21 → 26/02/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Agriculture
Family farm
Farming
Machine learning
Price prediction

Access to Document

10.1007/978-981-16-2102-4_78

Cite this

Cerna, R., Tirado, E., & Bayona-Oré, S. (2022). Price Prediction of Agricultural Products: Machine Learning. In X.-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021 (pp. 879-887). (Lecture Notes in Networks and Systems; Vol. 217). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-2102-4_78

Cerna, Rino ; Tirado, Eduardo ; Bayona-Oré, Sussy. / Price Prediction of Agricultural Products : Machine Learning. Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2022. pp. 879-887 (Lecture Notes in Networks and Systems).

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Cerna, R, Tirado, E & Bayona-Oré, S 2022, Price Prediction of Agricultural Products: Machine Learning. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. Lecture Notes in Networks and Systems, vol. 217, Springer Science and Business Media Deutschland GmbH, pp. 879-887, 6th International Congress on Information and Communication Technology, ICICT 2021, Virtual, Online, 25/02/21. https://doi.org/10.1007/978-981-16-2102-4_78

Price Prediction of Agricultural Products: Machine Learning. / Cerna, Rino; Tirado, Eduardo; Bayona-Oré, Sussy.
Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer Science and Business Media Deutschland GmbH, 2022. p. 879-887 (Lecture Notes in Networks and Systems; Vol. 217).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Price Prediction of Agricultural Products

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AU - Cerna, Rino

AU - Tirado, Eduardo

AU - Bayona-Oré, Sussy

PY - 2022

Y1 - 2022

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AB - Family farming is essentially characterized by the use of family labor force, due to the lack of land, water, and capital resources. An important tool is which allows them to know which products will be the best priced when production is completed, and at this point machine learning technology has, in particular, models and algorithms that allow for price prediction. The aim of this work is to review the literature related to price prediction of agricultural products using machine learning technology with the purpose of identifying the prediction models used in the studies. It also aims to identify the agricultural products used in these predictions to discuss their application in other products. The results show that neural network model is the most used in the selected studies.

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Cerna R, Tirado E, Bayona-Oré S. Price Prediction of Agricultural Products: Machine Learning. In Yang XS, Sherratt S, Dey N, Joshi A, editors, Proceedings of 6th International Congress on Information and Communication Technology, ICICT 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 879-887. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-16-2102-4_78

Price Prediction of Agricultural Products: Machine Learning

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