Research hotspots and development trends of harvesting robots based on bibliometric analysis and knowledge graphs

Over the past 30 years, there has been continuous progress in global science and technology. However, many agricultural products still heavily rely on traditional methods of manual and mechanical harvesting, facing challenges such as high costs and low efficiency. To address these challenges, researchers have developed various harvesting robots to handle diverse tasks in complex farm environments. This study analyzed pertinent papers on harvesting robots retrieved from the Web of Science (WOS) core database and the China National Knowledge Infrastructure (CNKI) database, spanning the years 1993 to 2022. Using specialized software such as CiteSpace and VOSviewer, a bibliometric analysis was conducted to examine the research progress and hotspots in the field of harvesting robots. The analysis of 517 English papers indicated a continuous expansion in the research scale of harvesting robots. Furthermore, the research history can be divided into three distinct periods. Currently, research on harvesting robots is experiencing a rapid growth phase, with the number of related papers steadily increasing each year. In the year 2022 alone, 151 English papers were published. This growth is attributed to close collaborations among different countries/regions, institutions, and authors. China, the United States, and Japan play crucial roles in the research of harvesting robots. Notably, China has published 326 English papers, ranking first globally. Through analysis, it was also found that Chinese papers focused on harvesting robots earlier, thereby promoting the development of agricultural robots. Additionally, bibliometric analysis revealed that the research hotspots of harvesting robots mainly include system and structure design, object recognition and localization, and multi-robot coordination, among others. In the future, development trends of harvesting robots will focus on: 1) diversifying robot types, 2) expanding application scenarios,3) enhancing overall performance to reduce losses, and 4) reducing manufacturing costs. In conclusion, through a comprehensive bibliometric analysis, this study has provided valuable insights to advance the automation of harvesting.