The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
Open accessAbstract
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.
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Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
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Greedy function approximation: A gradient boosting machine
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Classification and Regression by randomForest
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A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273 · cited 1×
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BackgroundInfluentialWhat is a support vector machine?
Noble, W. S. (2006) · Nature Biotechnology · doi:10.1038/nbt1206-1565 · Cited by 4,301
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Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017) · 2017 International Conference on Engineering and Technology (ICET) · doi:10.1109/icengtechnol.2017.8308186 · Cited by 3,986
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Metadata
- Authors
- Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J.
- Venue
- Agronomy
- Publisher
- MDPI AG
- Published
- 2023-12-01
- Volume
- 13
- Issue
- 12
- Pages
- 2976
- ISSN
- 2073-4395 (ISSN-L)
- OpenAlex
- W4389237989
- Cited by
- 259
- References
- 70
- FWCI
- 36.46
- Type
- journal article
- Language
- en
- Open access
- gold
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