Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
University of Lisbon · Uninova · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 79.62
- Percentile
- 100%
- References
- 92
Authors
5- SOSara Oleiro AraújoCorresponding
University of Lisbon, Uninova
- RSRicardo Silva Peres
Uninova, Universidade Nova de Lisboa
- JCJosé C. Ramalho
University of Lisbon, Universidade Nova de Lisboa
- FCFernando C. Lidon
University of Lisbon, Universidade Nova de Lisboa
- JBJosé BarataCorresponding
Uninova, Universidade Nova de Lisboa
Topics & keywords
- Agriculture
- Transformative learning
- Artificial intelligence
- Computer science
- Big data
- Sustainability
- Productivity
- Systematic review
- Zero hunger