TY - JOUR
T1 - Machine Learning Applications in Agriculture
T2 - Current Trends, Challenges, and Future Perspectives
AU - Araújo, Sara Oleiro
AU - Peres, Ricardo Silva
AU - Ramalho, José Cochicho
AU - Lidon, Fernando
AU - Barata, José
N1 - Funding Information:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04035%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT#
This work was supported in part by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through the research units UNINOVA-CTS (UIDB/00066/2020), GeoBioTec (UIDP/04035/2020), CEF (UIDB/00239/2020), and the Associate Laboratory TERRA (LA/P/0092/2020).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.
AB - Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field.
KW - Agriculture 4.0
KW - machine learning
KW - PRISMA
KW - systematic reviews and meta analytics
UR - http://www.scopus.com/inward/record.url?scp=85180676919&partnerID=8YFLogxK
U2 - 10.3390/agronomy13122976
DO - 10.3390/agronomy13122976
M3 - Review article
AN - SCOPUS:85180676919
SN - 2073-4395
VL - 13
JO - Agronomy
JF - Agronomy
IS - 12
M1 - 2976
ER -
