Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement
This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-09-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2075-1702/6/3/38 |
| _version_ | 1818137450842685440 |
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| author | Fabrizio Balducci Donato Impedovo Giuseppe Pirlo |
| author_facet | Fabrizio Balducci Donato Impedovo Giuseppe Pirlo |
| author_sort | Fabrizio Balducci |
| collection | DOAJ |
| description | This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it. |
| first_indexed | 2024-12-11T09:56:30Z |
| format | Article |
| id | doaj.art-ef779e07c0a443bbba6480a4c4e7c097 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-12-11T09:56:30Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-ef779e07c0a443bbba6480a4c4e7c0972022-12-22T01:12:15ZengMDPI AGMachines2075-17022018-09-01633810.3390/machines6030038machines6030038Machine Learning Applications on Agricultural Datasets for Smart Farm EnhancementFabrizio Balducci0Donato Impedovo1Giuseppe Pirlo2Dipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125 Bari, ItalyDipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125 Bari, ItalyDipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125 Bari, ItalyThis work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.http://www.mdpi.com/2075-1702/6/3/38machine learningsensorsIoTsmart farmsagriculturedata analysis |
| spellingShingle | Fabrizio Balducci Donato Impedovo Giuseppe Pirlo Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement Machines machine learning sensors IoT smart farms agriculture data analysis |
| title | Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement |
| title_full | Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement |
| title_fullStr | Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement |
| title_full_unstemmed | Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement |
| title_short | Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement |
| title_sort | machine learning applications on agricultural datasets for smart farm enhancement |
| topic | machine learning sensors IoT smart farms agriculture data analysis |
| url | http://www.mdpi.com/2075-1702/6/3/38 |
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