Evolution and Trend of Deep Learning in Agriculture: A Bibliometric Approach

作     者：Kimba Sabi N’goye Henoc Soude Yêyinou Laura Estelle Loko Kimba Sabi N’goye;Henoc Soude;Yêyinou Laura Estelle Loko

作者机构：Institute of Mathematics and Physics Porto-Novo Benin High School of Applied Biosciences and Biotechnologies (ENSBBA) Dassa-Zoumé Benin 

出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)

年 卷 期：2022年第10卷第12期

页      面：113-124页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Machine Learning Deep Learning Agriculture Bibliometric Africa 

摘      要：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.