Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/13/12/2976 |
| _version_ | 1797382354259410944 |
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| author | Sara Oleiro Araújo Ricardo Silva Peres José Cochicho Ramalho Fernando Lidon José Barata |
| author_facet | Sara Oleiro Araújo Ricardo Silva Peres José Cochicho Ramalho Fernando Lidon José Barata |
| author_sort | Sara Oleiro Araújo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T21:04:10Z |
| format | Article |
| id | doaj.art-11f6ee75667a424592098acc562c8cba |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-08T21:04:10Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-11f6ee75667a424592098acc562c8cba2023-12-22T13:46:31ZengMDPI AGAgronomy2073-43952023-12-011312297610.3390/agronomy13122976Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future PerspectivesSara Oleiro Araújo0Ricardo Silva Peres1José Cochicho Ramalho2Fernando Lidon3José Barata4UNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Monte de Caparica, 2829-516 Caparica, PortugalUNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Monte de Caparica, 2829-516 Caparica, PortugalGeoBioSciences, GeoTechnologies and GeoEngineering Unit (GeoBiotec), School of Sciences and Technology (NOVA-SST), 2829-516 Caparica, PortugalEarth Sciences Department (DCT), School of Sciences and Technology (NOVA-SST), NOVA University of Lisbon, 2829-516 Caparica, PortugalUNINOVA—Centre of Technology and Systems (CTS), FCT Campus, Monte de Caparica, 2829-516 Caparica, PortugalProgress 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.https://www.mdpi.com/2073-4395/13/12/2976Agriculture 4.0machine learningPRISMAsystematic reviews and meta analytics |
| spellingShingle | Sara Oleiro Araújo Ricardo Silva Peres José Cochicho Ramalho Fernando Lidon José Barata Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives Agronomy Agriculture 4.0 machine learning PRISMA systematic reviews and meta analytics |
| title | Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives |
| title_full | Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives |
| title_fullStr | Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives |
| title_full_unstemmed | Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives |
| title_short | Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives |
| title_sort | machine learning applications in agriculture current trends challenges and future perspectives |
| topic | Agriculture 4.0 machine learning PRISMA systematic reviews and meta analytics |
| url | https://www.mdpi.com/2073-4395/13/12/2976 |
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