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Machines 2018, 6(3), 38; https://doi.org/10.3390/machines6030038

Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Dipartimento di Informatica, Università degli studi di Bari Aldo Moro, 70125 Bari, Italy
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Received: 22 June 2018 / Revised: 11 August 2018 / Accepted: 14 August 2018 / Published: 1 September 2018
(This article belongs to the Special Issue Multi-Body System Dynamics: Monitoring, Simulation and Control)
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Abstract

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. View Full-Text
Keywords: machine learning; sensors; IoT; smart farms; agriculture; data analysis machine learning; sensors; IoT; smart farms; agriculture; data analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Balducci, F.; Impedovo, D.; Pirlo, G. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines 2018, 6, 38.

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