Publication:
FARMIT: Continuous Assessment of Crop Quality Using Machine Learning and Deep Learning Techniques for IoT-based Smart Farming

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Date
2022-03-31
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Authors
Perales Gómez, Ángel Luis ; López de Teruel Alcolea, Pedro Enrique ; Ruiz García, Alberto ; García Mateos, Ginés ; García Clemente, Félix Jesús
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Publisher
Springer
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Ingeniería y Tecnología de Computadores
DOI
https://doi.org/10.1007/s10586-021-03489-9
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info:eu-repo/semantics/article
Description
©<2022>. This manuscript version is made available under the CC-BY license http://creativecommons.org/licenses/ccby/4.0/ This document is the Acepted version of a Published Work that appeared in final form in [Cluster Computing]. To access the final edited and published work see [https://doi.org/ 10.1007/s10586-021-03489-9]
Abstract
The race for automation has reached farms and agricultural fields. Many of these facilities use the Internet of Things (IoT) technologies to automate processes and increase productivity. Besides, Machine Learning and Deep Learning allow performing continuous decision making based on data analysis. In this work, we fill a gap in the literature and present a novel architecture based on IoT and Machine Learning / Deep Learning technologies or the continuous assessment of agricultural crop quality. This architecture is divided into three layers that work together to gather, process, and analyze data from different sources to evaluate crop quality. In the experiments, he proposed approach based on data aggregation from different sources reaches a lower percentage error than considering only one source. In particular, the percentage error achieved by our approach in the test dataset was 6.59, while the percentage error achieved exclusively using data from sensors was 6.71.
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Crop quality , Deep learning , Internet of things , Machine learning , Smart farming
Citation
Cluster Computing, Volume 25, pages 2163–2178, (2022)
URI
http://hdl.handle.net/10201/138919
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