TITLE:
Evolution and Trend of Deep Learning in Agriculture: A Bibliometric Approach
AUTHORS:
Kimba Sabi N’goye, Henoc Soude, Yêyinou Laura Estelle Loko
KEYWORDS:
Machine Learning, Deep Learning, Agriculture, Bibliometric, Africa
JOURNAL NAME:
Journal of Computer and Communications,
Vol.10 No.12,
December
30,
2022
ABSTRACT: Deep Learning has recently gained a great deal of attention. From this, resulted many applications in a variety of industries, including agriculture. An essential study goal is to understand what has been done in the use of deep learning in agriculture (DLA) thus far in order to establish a robust research agenda to address its future challenges. The present state of research on the DLA with special attention to Africa was evaluated in this study using bibliometric analysis. A search of documents dealing with DLA was realized in the Web of Science database, a world-leading publisher-independent global citation database. A bibliometric program named Bibliometrix was used to examine the data after the search yielded 3207 items. Key findings are highlighted and discussed, and then some directions for potential future research are suggested.