Home > Archive>Volume 42, Issue 3, 2023 >29-38. DOI:10.13300/j.cnki.hnlkxb.2023.03.004
PDF HTML XML Export Cite reminder
Current status and trend of studying smart agriculture based on bibliometric analysis
CSTR:
[cstr]
Author:
Affiliation:

Library of Huazhong Agricultural University， Wuhan 430070， China

Clc Number:

S126;G250.73

  • Article
    • | |
  • Metrics
    • |
  • Reference [63]
    • |
  • Related [20]
    • | | |
  • Comments
    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， research topics， and cutting-edge hotspots of smart agriculture to provide reference and guidance for the development 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 development 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 agriculture included remote sensing， artificial intelligence， drones， the Internet of Things， and big data. smart agriculture can be divided into three major research topics including modern biotechnology represented by biological big data， information technology represented by the Internet of Things， artificial intelligence and remote sensing， intelligent agricultural machinery and equipment represented by drones and agricultural robots. The development of smart agriculture is a process of interdisciplinary integration to achieve highly precise， 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 technologies. It was proposed to achieve the agricultural transformation and upgrading in China by laying out key areas， cultivating new application-oriented talents， and developing original innovation.

    Reference
    [1] 赵春江.智慧农业发展现状及战略目标研究[J].智慧农业,2019(1):1-7.ZHAO C J.State-of-the-art and recommended developmental strategic objectivs of smart agriculture[J].Smart agriculture,2019(1):1-7 (in Chinese with English abstract).
    [2] 罗锡文,廖娟,臧英,等.我国农业生产的发展方向:从机械化到智慧化[J].中国工程科学,2022,24(1):46-54.LUO X W,LIAO J,ZANG Y,et al.Developing from mechanized to smart agricultural production in China[J].Strategic study of CAE,2022,24(1):46-54 (in Chinese with English abstract).
    [3] YANG X,SHU L,CHEN J N,et al.A survey on smart agriculture:development modes,technologies,and security and privacy challenges[J].IEEE/CAA journal of automatica sinica,2020,8(2):273-302.
    [4] 汪懋华.助力乡村振兴推进“智慧农业”创新发展[J].智慧农业,2019,1(1):3.WANG M H.Helping rural revitalization and promoting the innovative development of “smart agriculture”[J].Smart agriculture,2019,1(1):3 (in Chinese).
    [5] JAYARAMAN P P,YAVARI A,GEORGAKOPOULOS D,et al.Internet of Things platform for smart farming:experiences and lessons learnt[J/OL].Sensors (Basel,Switzerland),2016,16(11):1884[2023-03-01].https://doi.org/10.3390/s1611188.4.
    [6] 李道亮.物联网与智慧农业[J].农业工程,2012,2(1):1-7.LI D L.Internet of Things and wisdom agriculture[J].Agricultural engineering,2012,2(1):1-7 (in Chinese with English abstract).
    [7] 赵春江.智慧农业的发展现状与未来展望[J].华南农业大学学报,2021,42(6):1-7.ZHAO C J.Development status and future prospect of smart agriculture[J].Journal of South China Agricultural University,2021,42(6):1-7 (in Chinese with English abstract).
    [8] 罗锡文,廖娟,胡炼,等.我国智能农机的研究进展与无人农场的实践[J].华南农业大学学报,2021,42(6):8-17,5.LUO X W,LIAO J,HU L,et al.Research progress of intelligent agricultural machinery and practice of unmanned farm in China[J].Journal of South China Agricultural University,2021,42(6)8-17,5(in Chinese with English abstract).
    [9] 胡静涛,高雷,白晓平,等.农业机械自动导航技术研究进展[J].农业工程学报,2015,31(10):1-10.HU J T,GAO L,BAI X P,et al.Review of research on automatic guidance of agricultural vehicles[J].Transactions of the CSAE,2015,31(10):1-10 (in Chinese with English abstract).
    [10] YANG W N,FENG H,ZHANG X H,et al.Crop phenomics and high-throughput phenotyping:past decades,current challenges,and future perspectives[J].Molecular plant,2020,13(2):187-214.
    [11] 邱均平.文献计量学的定义及其研究对象[J].中国图书馆学报,1986,12(2):71.QIU J P.Definition of bibliometrics and its research object[J].Journal of library science in China,1986,12(2):71(in Chinese).
    [12] 任妮,郭婷,孙艺伟,等.全球智慧农业领域研究态势分析[J].农业图书情报学报,2021(9):48-63.REN N,GUO T,SUN Y W,et al.An analysis of global smart agriculture research situation[J].Journal of library and information science in agriculture,2021(9):48-63 (in Chinese with English abstract).
    [13] 杨道邦,林婕虹,邓杰,等.基于CiteSpace的国内外智慧农业研究进展[J].广东农业科学,2021,48(4):140-150.YANG D B,LIN J H,DENG J,et al.Research progress of smart agriculture based on CiteSpace at home and abroad[J].Guangdong agricultural sciences,2021,48(4):140-150(in Chinese with English abstract).
    [14] 洪帅,王天尊,符晓艺.中国智慧农业研究演进脉络梳理及前沿趋势分析[J].江苏农业科学,2023,51(4):28-38.HONG S,WANG T Z,FU X Y.Research evolution and frontier trend analysis of smart agriculture in China[J].Jiangsu agricultural sciences,2023,51(4):28-38(in Chinese).
    [15] WEISS M,JACOB F,DUVEILLER G.Remote sensing for agricultural applications:a meta-review[J/OL].Remote sensing of environment,2020,236:111402[2023-03-01].https://doi.org/10.1016/j.rse.2019.111402.
    [16] BOURSIANIS A D,PAPADOPOULOU M S,DIAMANTOULAKIS P,et al.Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming:a comprehensive review[J/OL].Internet of Things,2022,18:100187[2023-03-01].https://doi.org/10.1016/j.iot.2020.100187.
    [17] JUNG J,MAEDA M,CHANG A J,et al.The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems[J].Current opinion in biotechnology,2021,70:15-22.
    [18] 周济,陈佳玮,沈利言,等.人工智能——推动植物研究发展的新动力[J].南京农业大学学报,2022,45(5):1060-1071. ZOU J, CHEN J W, SHEN L Y, et al. Artificial intelligence: advancing plant research beyond the state of the art[J]. Journal of Nanjing Agricultural University, 2022,45(5):1060-1071 (in Chinese with English abstract).
    [19] JANIESCH C,ZSCHECH P,HEINRICH K.Machine learning and deep learning[J].Electronic markets,2021,31(3):685-695.
    [20] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
    [21] 李道亮,杨昊.农业物联网技术研究进展与发展趋势分析[J].农业机械学报,2018,49(1):1-20. LI D L, YANG H. State-of-the-art review for internet of things in agriculture[J]. Transactions of the Chinese society for agricultural machinery, 2018,49(1):1-20 (in Chinese with English abstract).
    [22] 张学工,江瑞,汪小我,等.从生物大数据到知识大发现:10年进展与未来展望[J].科学通报,2016,61(36):3869-3877.ZHANG X G,JIANG R,WANG X W,et al.From big biological data to big discovery:the past decade and the future[J].Chinese science bulletin,2016,61(36):3869-3877 (in Chinese with English abstract).
    [23] LI D L,QUAN C Q,SONG Z Y,et al.High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field[J/OL].Frontiers in bioengineering and biotechnology,2021,8:623705[2023-03-01].https://doi.org/10.3389/fbioe.2020.623705.
    [24] ZANDER M,LEWSEY M G,CLARK N M,et al.Integrated multi-omics framework of the plant response to jasmonic acid[J].Nature plants,2020,6(3):290-302.
    [25] GUI S T,YANG L F,LI J B,et al.ZEAMAP,a comprehensive database adapted to the maize multi-omics era[J/OL].iScience,2020,23(6):101241[2022-03-01].https://doi.org/10.1016/j.isci.2020.101241.
    [26] MEUWISSEN T H E,HAYES B J,GODDARD M E.Prediction of total genetic value using genome-wide dense marker maps[J].Genetics,2001,157(4):1819-1829.
    [27] LORENZANA R E,BERNARDO R.Accuracy of genotypic value predictions for marker-based selection in biparental plant populations[J].Theoretical and applied genetics,2009,120(1):151-161.
    [28] MCGOWAN M, WANG J, DONG H, et al. Ideas in genomic selection with the potential to transform plant molecula breeding: a review[J]. Plant breeding reviews, 2021,45: 273-319.
    [29] XU Y B,LIU X G,FU J J,et al.Enhancing genetic gain through genomic selection:from livestock to plants[J /OL].Plant communications,2020,1(1):100005[2023-03-01].https://doi.org/10.1016/j.xplc.2019.100005.
    [30] 张颖,廖生进,王璟璐等.信息技术与智能装备助力智能设计育种[J].吉林农业大学学报,2021,43(2):119-129. ZHANG Y, LIAO S J, WANG J L, et al. Information technology and intelligent equipment facilitating smart breeding[J]. Journal of Jilin Agricultural University, 2021,43(2):119-129 (in Chinese with English abstract).
    [31] WALLACE J G,RODGERS-MELNICK E,BUCKLER E S.On the road to breeding 4.0:unraveling the good,the bad,and the boring of crop quantitative genomics[J].Annual review of genetics,2018,52:421-444.
    [32] FRIHA O,FERRAG M A,SHU L,et al.Internet of Things for the future of smart agriculture:a comprehensive survey of emerging technologies[J].IEEE/CAA journal of automatica sinica,2021,8(4):718-752.
    [33] PHASINAM K, KASSANUK T, SHABAZ M. Applicability of Internet of Things in smart farming[J/OL]. Journal of food quality, 2022:7692922[2023-03-01].https://doi.org/10.1155/2022/7692922.
    [34] BOUZEMBRAK Y,KLüCHE M,GAVAI A,et al.Internet of Things in food safety:literature review and a bibliometric analysis[J].Trends in food science & technology,2019,94:54-64.
    [35] ALKHATEEB A,CATAL C,KAR G,et al.Hybrid blockchain platforms for the Internet of Things (IoT):a systematic literature review[J/OL].Sensors,2022,22(4):1304[2023-03-01].https://doi.org/10.3390/s22041304.
    [36] SALAH K,NIZAMUDDIN N,JAYARAMAN R,et al.Blockchain-based soybean traceability in agricultural supply chain[J].IEEE access,2019,7:73295-73305.
    [37] WANG J, ZHANG X, XU J P, et al. Blockchain-based information supervision model for rice supply chains[J/OL]. Computational intelligence and neuroscienc, 2022:2914571[2023-03-01].https://doi.org/10.1155/2022/2914571.
    [38] DíAZ M,MARTíN C,RUBIO B.State-of-the-art,challenges,and open issues in the integration of Internet of Things and cloud computing[J].Journal of network and computer applications,2016,67:99-117.
    [39] JIN X J,SUN Y X,CHE J,et al.A novel deep learning-based method for detection of weeds in vegetables[J].Pest management science,2022,78(5):1861-1869.
    [40] JIANG Z,TU H F,BAI B W,et al.Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress[J].New phytologist,2021,232(1):440-455.
    [41] WU H,WIESNER-HANKS T,STEWART E L,et al.Autonomous detection of plant disease symptoms directly from aerial imagery[J].The plant phenome journal,2019,2(1):1-9.
    [42] MA Y C,ZHANG Z,KANG Y H,et al.Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach[J/OL].Remote sensing of environment,2021,259:112408[2023-03-01].https://doi.org/10.1016/j.rse.2021.112408.
    [43] CHEN T C, YU S Y. The review of food safety inspection system based on artificial intelligence, image processing, and robotic[J]. Food science and technology, 2022, 42(15):45-46.
    [44] TAMIMINIA H,SALEHI B,MAHDIANPARI M,et al.Google Earth Engine for geo-big data applications:a meta-analysis and systematic review[J].ISPRS journal of photogrammetry and remote sensing,2020,164:152-170.
    [45] ZHANG X,WU B F,PONCE-CAMPOS G,et al.Mapping up-to-date paddy rice extent at 10 M resolution in China through the integration of optical and synthetic aperture radar images[J/OL].Remote sensing,2018,10(8):1200[2023-03-01].https://doi.org/10.3390/rs10081200.
    [46] LI H P,ZHANG C,ZHANG S Q,et al.Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the random forest algorithm[J/OL].International journal of applied earth observation and geoinformation,2020,87:102032[2023-03-01].https://doi.org/10.1016/j.jag.2019.102032.
    [47] ZHAN P,ZHU W Q,LI N.An automated rice mapping method based on flooding signals in synthetic aperture radar time series[J/OL].Remote sensing of environment,2021,252:112112[2023-03-01].https://doi.org/10.1016/j.rse.2020.112112.
    [48] WANG J J,WU F,SHANG J L,et al.Saline soil moisture mapping using Sentinel-1A synthetic aperture radar data and machine learning algorithms in humid region of China’s east coast[J/OL].CATENA,2022,213:106189[2023-03-01].https://doi.org/10.1016/j.catena.2022.106189.
    [49] MALAMBO L,POPESCU S C,MURRAY S C,et al.Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery[J].International journal of applied earth observation and geoinformation,2018,64:31-42.
    [50] DENG L,MAO Z H,LI X J,et al.UAV-based multispectral remote sensing for precision agriculture:a comparison between different cameras[J].ISPRS journal of photogrammetry and remote sensing,2018,146:124-136.
    [51] XU R,LI C Y,BERNARDES S.Development and testing of a UAV-based multi-sensor system for plant phenotyping and precision agriculture[J/OL].Remote sensing,2021,13(17):3517[2023-03-01]. https://doi.org/10.3390/rs13173517.
    [52] VIRLET N, SABERMANESH K, SADEGHI-TEHRAN P, et al. Field scanalyzer:an automated robotic field phenotyping platform for detailed crop monitoring[J]. Functional plant biology,2016,44(1):143-153.
    [53] TANG Y C,CHEN M Y,WANG C L,et al.Recognition and localization methods for vision-based fruit picking robots:a review[J/OL].Frontiers in plant science,2020,11:510[2023-03-01]. https://doi.org/10.3389/fpls.2020.00510.
    [54] YAN B,FAN P,LEI X Y,et al.A real-time apple targets detection method for picking robot based on improved YOLOv5[J/OL].Remote sensing,2021,13(9):1619[2023-03-01].https://doi.org/10.3390/rs13091619.
    [55] YANG S T,GU L J,LI X F,et al.Crop classification method based on optimal feature selection and hybrid CNN-RF networks for multi-temporal remote sensing imagery[J/OL].Remote sensing,2020,12(19):3119[2023-03-01].https://doi.org/10.3390/rs12193119.
    [56] ZHANG Y,SHAO Z F.Assessing of urban vegetation biomass in combination with LiDAR and high-resolution remote sensing images[J].International journal of remote sensing,2021,42(3):964-985.
    [57] SHI W S,CAO J,ZHANG Q,et al.Edge computing:vision and challenges[J].IEEE Internet of Things journal,2016,3(5):637-646.
    [58] REN J,ZHANG D Y,HE S W,et al.A survey on end-edge-cloud orchestrated network computing paradigms:transparent computing,mobile edge computing,fog computing,and cloudlet[J].ACM computing surveys,2019,52(6):1-36.
    [59] FIROUZI F,FARAHANI B,MARIN?EK A.The convergence and interplay of edge,fog,and cloud in the AI-driven Internet of Things (IoT)[J/OL].Information systems,2022,107:101840[2023-03-01].https://doi.org/10.1016/j.is.2021.101840.
    [60] 兰玉彬, 王天伟, 陈盛德, 等. 农业人工智能技术:现代农业科技的翅膀[J]. 华南农业大学学报, 2020, 41(6):1-13.LAN Y B, WANG T W, CHEN S D, et al. Agricultural artificial intelligence technology:wings of modern agricultural science and technology[J]. Journal of South China Agricultural University, 2020, 41(6):1-13(in Chinese with English abstract).
    [61] MA W L,QIU Z X,SONG J,et al.A deep convolutional neural network approach for predicting phenotypes from genotypes[J].Planta,2018,248(5):1307-1318.
    [62] LIU Y,WANG D L,HE F,et al.Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean[J/OL].Frontiers in genetics,2019,10:1091[2023-03-01].https://doi.org/10.3389/fgene.2019.01091.
    [63] SANDHU K S,LOZADA D N,ZHANG Z W,et al.Deep learning for predicting complex traits in spring wheat breeding program[J/OL].Frontiers in plant science,2021,11:613325[2023-03-01].https://doi.org/10.3389/fpls.2020.613325.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

郑倩,李鹏云,周迪. Current status and trend of studying smart agriculture based on bibliometric analysis[J]. Jorunal of Huazhong Agricultural University,2023,42(3):29-38.

Copy
Share
Article Metrics
  • Abstract:644
  • PDF: 2297
  • HTML: 1498
  • Cited by: 0
History
  • Received:March 01,2023
  • Online: June 20,2023
Article QR Code

Contact Us

Tel: 027-87287364
Postcode: 430070
E-mail：hnlkxb@mail.hzau.edu.cn
Address: No.1 Shizishan Street,Hongshan District,Wuhan, Hubei,China

Journal Subscription Email Alert ISSN:1000-2421 FPostal Code:BM 3816 CN:42-1181/S Domestic Code:38-120

Jorunal of Huazhong Agricultural University ® 2025 All Rights Reserved   Supported by:Beijing E-Tiller Technology Development Co., Ltd.