Accelerated ripening through the exposure of fruits to controlled environmental conditions and gases is nowadays one of the most assessed food technologies, especially for climacteric and exotic products. However, a fine granularity control of the process and consequently of the quality of the goods is still missing, so the management of the ripening rooms is mainly based on qualitative estimations only. Following the modern paradigms of Industry 4.0, this contribution proposes a non-destructive RFID-based system for the automatic evaluation of the live ripening of avocados. The system, coupled with a properly trained automatic classification algorithm based on Support Vector Machines (SVMs), can discriminate the stage of ripening with an accuracy greater than 85%.

Occhiuzzi, C., Camera, F., Drorazio, M., Druva, N., Amendola, S., Maria Bianco, G., et al. (2022). Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning. IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 6, 649-659 [10.1109/jrfid.2022.3174272].

Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning

Cecilia Occhiuzzi;Sara Amendola;Eugenio Martinelli;Gaetano Marrocco
2022-01-01

Abstract

Accelerated ripening through the exposure of fruits to controlled environmental conditions and gases is nowadays one of the most assessed food technologies, especially for climacteric and exotic products. However, a fine granularity control of the process and consequently of the quality of the goods is still missing, so the management of the ripening rooms is mainly based on qualitative estimations only. Following the modern paradigms of Industry 4.0, this contribution proposes a non-destructive RFID-based system for the automatic evaluation of the live ripening of avocados. The system, coupled with a properly trained automatic classification algorithm based on Support Vector Machines (SVMs), can discriminate the stage of ripening with an accuracy greater than 85%.
2022
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/02 - CAMPI ELETTROMAGNETICI
English
Monitoring
Radiofrequency identification
Antennas
Antenna measurements
Sensors
Humidity
Visualization
Automatic monitoring
RFID sensor
Industry 4
0
fruit ripening
Occhiuzzi, C., Camera, F., Drorazio, M., Druva, N., Amendola, S., Maria Bianco, G., et al. (2022). Automatic Monitoring of Fruit Ripening Rooms by UHF RFID Sensor Network and Machine Learning. IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION, 6, 649-659 [10.1109/jrfid.2022.3174272].
Occhiuzzi, C; Camera, F; Drorazio, M; Druva, N; Amendola, S; Maria Bianco, G; Miozzi, C; Garavaglia, L; Martinelli, E; Marrocco, G
Articolo su rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/310816
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