Price Prediction of Agricultural Products: Machine Learning

Rino Cerna, Eduardo Tirado, Sussy Bayona-Oré

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaProceedings of 6th International Congress on Information and Communication Technology, ICICT 2021
EditoresXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas879-887
Número de páginas9
ISBN (versión impresa)9789811621017
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online
Duración: 25 feb. 202126 feb. 2021

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen217
ISSN (versión impresa)2367-3370
ISSN (versión digital)2367-3389

Conferencia

Conferencia6th International Congress on Information and Communication Technology, ICICT 2021
CiudadVirtual, Online
Período25/02/2126/02/21

Nota bibliográfica

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

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