Machine Learning in Agriculture: A Comprehensive Updated Review
The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potent...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/11/3758 |
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| author | Lefteris Benos Aristotelis C. Tagarakis Georgios Dolias Remigio Berruto Dimitrios Kateris Dionysis Bochtis |
| author_facet | Lefteris Benos Aristotelis C. Tagarakis Georgios Dolias Remigio Berruto Dimitrios Kateris Dionysis Bochtis |
| author_sort | Lefteris Benos |
| collection | DOAJ |
| description | The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic. |
| first_indexed | 2024-03-10T10:56:10Z |
| format | Article |
| id | doaj.art-fd6a6c00f47f4b1f872b7d8507665452 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:56:10Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fd6a6c00f47f4b1f872b7d85076654522023-11-21T21:52:54ZengMDPI AGSensors1424-82202021-05-012111375810.3390/s21113758Machine Learning in Agriculture: A Comprehensive Updated ReviewLefteris Benos0Aristotelis C. Tagarakis1Georgios Dolias2Remigio Berruto3Dimitrios Kateris4Dionysis Bochtis5Centre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, GreeceCentre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, GreeceCentre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, GreeceDepartment of Agriculture, Forestry and Food Science (DISAFA), University of Turin, Largo Braccini 2, 10095 Grugliasco, ItalyCentre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, GreeceCentre of Research and Technology-Hellas (CERTH), Institute for Bio-Economy and Agri-Technology (IBO), 6th km Charilaou-Thermi Rd, GR 57001 Thessaloniki, GreeceThe digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.https://www.mdpi.com/1424-8220/21/11/3758machine learningcrop managementwater managementsoil managementlivestock managementartificial intelligence |
| spellingShingle | Lefteris Benos Aristotelis C. Tagarakis Georgios Dolias Remigio Berruto Dimitrios Kateris Dionysis Bochtis Machine Learning in Agriculture: A Comprehensive Updated Review Sensors machine learning crop management water management soil management livestock management artificial intelligence |
| title | Machine Learning in Agriculture: A Comprehensive Updated Review |
| title_full | Machine Learning in Agriculture: A Comprehensive Updated Review |
| title_fullStr | Machine Learning in Agriculture: A Comprehensive Updated Review |
| title_full_unstemmed | Machine Learning in Agriculture: A Comprehensive Updated Review |
| title_short | Machine Learning in Agriculture: A Comprehensive Updated Review |
| title_sort | machine learning in agriculture a comprehensive updated review |
| topic | machine learning crop management water management soil management livestock management artificial intelligence |
| url | https://www.mdpi.com/1424-8220/21/11/3758 |
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