Page 44 - 《华中农业大学学报（自然科学版）》2023年第3期

38 华 中 农 业 大 学 学 报 第 42 卷

cloud orchestrated network computing paradigms：transparent 2020， 41（6）：1-13（in Chinese with English abstract）.
computing，mobile edge computing，fog computing，and cloudlet ［61］ MA W L，QIU Z X，SONG J，et al.A deep convolutional neural
［J］.ACM computing surveys，2019，52（6）：1-36. network approach for predicting phenotypes from genotypes［J］.
［59］ FIROUZI F，FARAHANI B，MARINŠEK A.The convergence Planta，2018，248（5）：1307-1318.
and interplay of edge，fog，and cloud in the AI-driven Internet of ［62］ LIU Y，WANG D L，HE F，et al.Phenotype prediction and ge‐
Things （IoT）［J/OL］. Information systems，2022，107：101840 nome-wide association study using deep convolutional neural net‐
［2023-03-01］.https：//doi.org/10.1016/j.is.2021.101840. work of soybean［J/OL］. Frontiers in genetics，2019，10：1091
［60］ 兰玉彬， 王天伟， 陈盛德， 等 . 农业人工智能技术：现代农业科 ［2023-03-01］.https：//doi.org/10.3389/fgene.2019.01091.
技的翅膀［J］. 华南农业大学学报， 2020， 41（6）：1-13.LAN Y ［63］ SANDHU K S，LOZADA D N，ZHANG Z W，et al.Deep learn‐
B， WANG T W， CHEN S D， et al. Agricultural artificial intelli‐ ing for predicting complex traits in spring wheat breeding pro‐
gence technology：wings of modern agricultural science and tech‐ gram［J/OL］.Frontiers in plant science，2021，11：613325［2023-
nology［J］. Journal of South China Agricultural University， 03-01］.https：//doi.org/10.3389/fpls.2020.613325.

Current status and trend of studying smart agriculture

based on bibliometric analysis

ZHENG Qian,LI Pengyun,ZHOU Di
Library of Huazhong Agricultural University， Wuhan 430070， China

Abstract Smart agriculture integrates modern information technology， agricultural machinery and

equipment， and biotechnology， which is the development trend of modern agriculture. China is in the early
stage of transitioning from traditional agriculture to intelligent agriculture. This article used the bibliometric
analysis to analyze 40 812 relevant literatures in the field of global smart agriculture collected by the SCIE
database. A knowledge map was drawn to conduct in-depth analysis on the core elements of knowledge， re‐
search topics， and cutting-edge hotspots of smart agriculture to provide reference and guidance for the de‐
velopment and study of smart agriculture in China. Results showed that the number of publications in the
field of smart agriculture has increased significantly since 2016. China is the country with the fastest devel‐
opment in this field globally. Results of co-occurrence clustering analysis on keywords from 632 highly cited
papers on smart agriculture in the past decade showed that the core elements of knowledge in smart agricul‐
ture included remote sensing， artificial intelligence， drones， the Internet of Things， and big data. smart ag‐
riculture can be divided into three major research topics including modern biotechnology represented by bio‐
logical big data， information technology represented by the Internet of Things， artificial intelligence and re‐
mote sensing， intelligent agricultural machinery and equipment represented by drones and agricultural ro‐
bots. The development of smart agriculture is a process of interdisciplinary integration to achieve highly pre‐
cise， intelligent， and efficient agricultural production. Results of analyzing the evolution of keywords
showed that information perception， processing， and management represented by the Internet of Things，
as well as artificial intelligence algorithms represented by machine learning and deep learning， have been
cutting-edge hotspots in smart agriculture research in recent years. The development of smart agriculture in
the future was discussed from the perspectives of policy formulation， talent cultivation， and key technolo‐
gies. It was proposed to achieve the agricultural transformation and upgrading in China by laying out key ar‐
eas， cultivating new application-oriented talents， and developing original innovation.
Keywords smart agriculture； precision agriculture； bibliometric analysis； research status and trend；

visualization analysis
（责任编辑：张志钰）