Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review
Year: 2022
Type: preprint
Study design: Systematic Review
Abstract: This paper draws a systematic literature review about the use of Machine learning based recommender systems for crop selection, with respect to the PRISMA protocol for systematic reviews. The second section, describes an overview of existing recommender systems in literature. The outline of this study is explained, as well as the method of content analysis used in this article to sort out the papers is introduced in the third section. In the fourth section, the selection process and the literature review matrix are provided. Additionally, the evolvement of research on crop recommendation over the years is considered, a detailed study of the main input features is done. Further, the current challenges found in crop recommendation are listed. The fifth section discusses the outcomes of the study. The last section recapitulates the outcomes of the study, shedding the light of research perspectives worthwhile pursuing.
Source: Research Square
Authors: Younes OMMANE, Mohamed Amine RHANBOURI, Hicham CHOUIKH, Mourad JBENE, Ikram CHAIRI, Mohamed LACHGAR, Saad BENJELLOUN
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W4294891867