WSEAS Transactions on Business and Economics


Print ISSN: 1109-9526
E-ISSN: 2224-2899

Volume 18, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 18, 2021


Machine Learning Applied to Price Prediction in Agricultural Products

AUTHORS: Sussy Bayona-Oré, Rino Cerna, Eduardo Tirado Hinojoza

DOI: 10.37394/23207.2021.18.92
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ABSTRACT: Family farms play a role in economic development. Limited in terms of land, water and capital resources, family farming is essentially characterized by its use of family labour. Family farms must choose which agricultural products to produce; however, they do not have the necessary tools for optimizing their decisions. Knowing which products will have the best prices at harvest is important to farmers. At this point, machine learning technology has been used to solve classification and prediction problems, such as price prediction. This work aims to review the literature in this area related to price prediction for agricultural products and seeks to identify the research paradigms employed, the type of research used, the most commonly used algorithms and techniques for evaluation, and the agricultural products used in these predictions. The results show that the mostly commonly used research paradigm is positivism, the research is quantitative and longitudinal in nature and neural networks are the most commonly used algorithms.

KEYWORDS: Machine learning, Price prediction, Agriculture, Farming, Family farm

Wseas Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 18, 2021, Art. #92, pp. 969-977


Copyright Β© 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0