Forced ripening through the exposure of fruits to controlled environmental and gases conditions is nowadays one of the most assessed food technologies especially for climacteric and exotic products. However, a fine granularity control of the ripening process and consequently of the goods quality is still missing, so that the management of the ripening room is mainly based on qualitative estimations only. Following the modern paradigms of Industry 4.0, this contribution proposes a nondestructive RFID-based system for the automatic evaluation of the live ripening of up to 128 avocados inside the ripening room. The system, coupled with a properly trained automatic classification algorithm, is capable to discriminate the early stage of ripening with an accuracy greater than 85%.
Occhiuzzi, C., Camera, F., D'Uva, N., Amendola, S., Garavaglia, L., Marrocco, G. (2021). Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensors and Machine Learning. In 2021 IEEE International Conference on RFID Technology and Applications, RFID-TA 2021 (pp.187-190). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/RFID-TA53372.2021.9617278].
Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensors and Machine Learning
Occhiuzzi C.;Amendola S.;Marrocco G.
2021-01-01
Abstract
Forced ripening through the exposure of fruits to controlled environmental and gases conditions is nowadays one of the most assessed food technologies especially for climacteric and exotic products. However, a fine granularity control of the ripening process and consequently of the goods quality is still missing, so that the management of the ripening room is mainly based on qualitative estimations only. Following the modern paradigms of Industry 4.0, this contribution proposes a nondestructive RFID-based system for the automatic evaluation of the live ripening of up to 128 avocados inside the ripening room. The system, coupled with a properly trained automatic classification algorithm, is capable to discriminate the early stage of ripening with an accuracy greater than 85%.| File | Dimensione | Formato | |
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