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.
|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|
|Number of pages||9|
|State||Published - 2022|
|Event||6th International Congress on Information and Communication Technology, ICICT 2021 - Virtual, Online|
Duration: 25 Feb 2021 → 26 Feb 2021
|Name||Lecture Notes in Networks and Systems|
|Conference||6th International Congress on Information and Communication Technology, ICICT 2021|
|Period||25/02/21 → 26/02/21|
Bibliographical notePublisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Family farm
- Machine learning
- Price prediction