A high level representation of MeloDI architecture
Abstract:
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 ...Show MoreMetadata
Abstract:
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 Squa...
A high level representation of MeloDI architecture
Published in: IEEE Access ( Volume: 12)