Research Hotspots and Frontier Prospects in the Field of Agroforestry Picking Robots in China—Cite Space Bibliographic Analysis

Abstract

The research on picking robots is vital to the transformation and upgrading of the agroforestry industry and the revitalization and development of rural areas. This paper examines the research field of agroforestry picking robots by meticulously combing and analyzing 623 CNKI and 648 WoS core literature from 2004 to 2022 selected in China Knowledge Network (CNKI) and Web of Science (WoS) databases using Cite Space 6.1R3 software. The analysis includes the quantity of literature, issuing countries, organizations, keywords, keyword clustering, emerging terms, etc. On this basis, research hotspots in the field of agroforestry picking robots are identified, such as research based on the identification of picking targets, the control of motion planning, structural design and simulation, and the planning of walking paths. This paper analyzes and discusses these research hotspots and main lines, providing a reference for future studies in this field. This bibliometric approach can provide comprehensive literature information for research in related fields, as well as identify and summarize the major research hotspots in a shorter time, allowing new researchers to enter the field more quickly and obtain more valuable scientific information.

Tracing the Evolution of E-Government: A Visual Bibliometric Analysis from 2000 to 2023

“…Keywords are crucial in encapsulating the central themes and essence of a paper. Keyword co-occurrence analysis uses keywords as nodes to explore hot research areas within different periods (Jia et al 2023). In the knowledge map of keyword co-occurrence, larger node labels indicate higher frequencies of the corresponding keywords.…”

“…In other words, these metrics can be used to assess the effectiveness of the knowledge map of clusters. Normally, if the Q value is above 0.3 and the S value is above 0.5, the cluster can be deemed reasonable, and the divided community structure is significant (Chen et al 2022;Jia et al 2023).…”