Development Trends of Agricultural Engineering Technology Based on Bibliometrics

FU Longsheng, JIA Bo, LIU Xiaojuan, HE Leilei, YANG Liling, MAO Wulan, LI Rui

Abstract

Aiming to gain a macro understanding of development dynamics, frontiers, and research hotspots in agricultural engineering technology, and better promote progress in the field, a bibliometric analysis of related articles from January 2015 to June 2025 was conducted, using Web of Science Core Collection and CNKI databases. The main conclusions were as follows: agricultural engineering technology exhibited explosive growth, with annual publications in international journals rising from 64 in 2015 to 910 in 2024. Compared with the Chinese journals increase from 153 publications in 2015 to 288 in 2021, the international growth rate was significantly faster, with an average annual growth rate exceeding 30%, highlighting the vitality of research driven by frontier technologies, including artificial intelligence, robotics, and remote sensing. Collaboration among countries, particularly China, United States, and Brazil, was relatively close, with China and United States serving as the main drivers of agricultural engineering technology, jointly accounting for 75.36% of the total publications. However, international cross-team collaboration remained somewhat limited. Computers and Electronics in Agriculture (48.18%) is ranking the first in the international journals, Transactions of the Chinese Society of Agricultural Engineering (44.61%) and Transactions of the Chinese Society for Agricultural Machinery (32.69%) are the first two Chinese journals, leading among peer journals. Research themes in agricultural engineering gradually evolved from early-stage simple classification tasks to intelligent technologies, such as artificial intelligence, deep learning, and machine learning. In response to the complexity and interdisciplinary demands of agricultural engineering, future efforts should prioritize strengthening the algorithm layer by integrating artificial intelligence and big data technologies to enhance multi-source data modeling and data-driven decision-making, thereby enabling intelligent decision-making in agricultural systems. The perception layer should achieve precise acquisition of environmental and crop conditions through multimodal sensing and 3D reconstruction. The execution layer can leverage adaptive, low-cost smart agricultural machinery or robots to perform efficient operations, while the support layer should ensure system efficiency through edge computing, cloud services, and digital twins. By integrating key technologies such as multimodal sensing, 3D reconstruction, and digital twins, a closed-loop “perception-modeling-decision-execution” system can be established, providing strong support for the intelligent and sustainable development of agriculture.

 

Keywords: bibliometrics;database, agricultural engineering technology, intelligentization, smart agriculture

 

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