MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images
In the race for economic growth, many production activities have incorporated automated devices into their processes. This is also true for the agriculture sector, where different sensors and Internet of Things (IoT) architectures have been proposed to perform automatic data gathering and data analy...
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2024-01-01
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| author | Angel Luis Perales Gomez Juan Jesus Losada del Olmo Pedro E. Lopez-de-Teruel Alberto Ruiz Felix J. Garcia Clemente Andres Conesa Bueno |
| author_facet | Angel Luis Perales Gomez Juan Jesus Losada del Olmo Pedro E. Lopez-de-Teruel Alberto Ruiz Felix J. Garcia Clemente Andres Conesa Bueno |
| author_sort | Angel Luis Perales Gomez |
| collection | DOAJ |
| description | In the race for economic growth, many production activities have incorporated automated devices into their processes. This is also true for the agriculture sector, where different sensors and Internet of Things (IoT) architectures have been proposed to perform automatic data gathering and data analyzing in later stages. However, most architectures only consider data from one source, ignoring valuable information from other sources like images. Furthermore, the few solutions that consider information from images employ an approach where the cameras are fixed. In this paper, we propose an IoT architecture called MeloDI that gathers data from traditional IoT sensors and images taken by drones and applies Machine Learning (ML) techniques to evaluate melon quality. The images taken by the drone are both RGB and multispectral, which allow MeloDI to analyze critical growth information. MeloDI is powered by the Cloud and Edge Computing paradigm, and it is divided into three layers: physical, edge, and cloud. The physical layer comprises devices that get information from melons, including drones. The edge layer is responsible for sending the data from sensors to the cloud layer, receiving the corrective actions from the cloud layer, and enforcing them. Finally, the cloud layer is responsible for analyzing the data using ML techniques to assess the quality of the melons. Additionally, we deployed MeloDI in a melon plantation in Southeast Spain. MeloDI gathered data from sensors and drones, extracting new features and indicators to determine melon quality. For comparison purposes with other solutions that only accept one data source, we tested three different configurations of MeloDI: using only data from traditional sensors, using only data from images taken by drones, and using both data sources. We conclude that the configuration using both data sources outperforms the other configurations. In particular, the best-performing model was Random Forest that achieved a Mean Square Error of 1.6111 and a corresponding error rate of 7.9944% on the test set. |
| format | Article |
| id | doaj-art-eb6812c6d22e4d4d810cc66631f3ebd8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eb6812c6d22e4d4d810cc66631f3ebd82024-12-25T00:01:06ZengIEEEIEEE Access2169-35362024-01-011219383119384710.1109/ACCESS.2024.352000410806693MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone ImagesAngel Luis Perales Gomez0https://orcid.org/0000-0003-1004-881XJuan Jesus Losada del Olmo1https://orcid.org/0009-0005-8358-202XPedro E. Lopez-de-Teruel2https://orcid.org/0000-0001-7573-1738Alberto Ruiz3https://orcid.org/0000-0001-9107-1952Felix J. Garcia Clemente4https://orcid.org/0000-0001-6181-5033Andres Conesa Bueno5Department of Computer Engineering and Technology, University of Murcia, Murcia, SpainBleecker Technologies, Murcia, SpainDepartment of Computer Engineering and Technology, University of Murcia, Murcia, SpainDepartment of Informatics and Systems, University of Murcia, Murcia, SpainDepartment of Computer Engineering and Technology, University of Murcia, Murcia, SpainResearch Projects Office, JimboFresh International, Cartagena, SpainIn the race for economic growth, many production activities have incorporated automated devices into their processes. This is also true for the agriculture sector, where different sensors and Internet of Things (IoT) architectures have been proposed to perform automatic data gathering and data analyzing in later stages. However, most architectures only consider data from one source, ignoring valuable information from other sources like images. Furthermore, the few solutions that consider information from images employ an approach where the cameras are fixed. In this paper, we propose an IoT architecture called MeloDI that gathers data from traditional IoT sensors and images taken by drones and applies Machine Learning (ML) techniques to evaluate melon quality. The images taken by the drone are both RGB and multispectral, which allow MeloDI to analyze critical growth information. MeloDI is powered by the Cloud and Edge Computing paradigm, and it is divided into three layers: physical, edge, and cloud. The physical layer comprises devices that get information from melons, including drones. The edge layer is responsible for sending the data from sensors to the cloud layer, receiving the corrective actions from the cloud layer, and enforcing them. Finally, the cloud layer is responsible for analyzing the data using ML techniques to assess the quality of the melons. Additionally, we deployed MeloDI in a melon plantation in Southeast Spain. MeloDI gathered data from sensors and drones, extracting new features and indicators to determine melon quality. For comparison purposes with other solutions that only accept one data source, we tested three different configurations of MeloDI: using only data from traditional sensors, using only data from images taken by drones, and using both data sources. We conclude that the configuration using both data sources outperforms the other configurations. In particular, the best-performing model was Random Forest that achieved a Mean Square Error of 1.6111 and a corresponding error rate of 7.9944% on the test set.https://ieeexplore.ieee.org/document/10806693/Precise agriculturecrop qualitymachine learningartificial intelligencesmart farmingInternet of Things |
| spellingShingle | Angel Luis Perales Gomez Juan Jesus Losada del Olmo Pedro E. Lopez-de-Teruel Alberto Ruiz Felix J. Garcia Clemente Andres Conesa Bueno MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images IEEE Access Precise agriculture crop quality machine learning artificial intelligence smart farming Internet of Things |
| title | MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images |
| title_full | MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images |
| title_fullStr | MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images |
| title_full_unstemmed | MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images |
| title_short | MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images |
| title_sort | melodi an internet of things architecture to evaluate melon quality by means of machine learning using sensors data and drone images |
| topic | Precise agriculture crop quality machine learning artificial intelligence smart farming Internet of Things |
| url | https://ieeexplore.ieee.org/document/10806693/ |
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