TY - JOUR
T1 - Artificial intelligence-based decision support systems in smart agriculture
T2 - Bibliometric analysis for operational insights and future directions
AU - Yousaf, Arslan
AU - Kayvanfar, Vahid
AU - Mazzoni, Annamaria
AU - Elomri, Adel
N1 - Publisher Copyright:
Copyright © 2023 Yousaf, Kayvanfar, Mazzoni and Elomri.
PY - 2023/1/9
Y1 - 2023/1/9
N2 - As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.
AB - As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.
KW - Agriculture 4.0
KW - Internet of Things
KW - artificial intelligence
KW - bibliometric analysis
KW - machine learning
KW - operations research
KW - precision agriculture
KW - smart agriculture
UR - http://www.scopus.com/inward/record.url?scp=85146999384&partnerID=8YFLogxK
U2 - 10.3389/fsufs.2022.1053921
DO - 10.3389/fsufs.2022.1053921
M3 - Review article
AN - SCOPUS:85146999384
SN - 2571-581X
VL - 6
JO - Frontiers in Sustainable Food Systems
JF - Frontiers in Sustainable Food Systems
M1 - 1053921
ER -