Author(s)

Kimba Sabi N’goye¹, Henoc Soude¹, Yêyinou Laura Estelle Loko²

¹Institute of Mathematics and Physics, Porto-Novo, Benin.
²High School of Applied Biosciences and Biotechnologies (ENSBBA), Dassa-Zoumé, Benin.

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.

Machine Learning, Deep Learning, Agriculture, Bibliometric, Africa

N’goye, K. , Soude, H. and Loko, Y. (2022) Evolution and Trend of Deep Learning in Agriculture: A Bibliometric Approach. Journal of Computer and Communications, 10, 113-124. doi: 10.4236/jcc.2022.1012009.