There are many varieties of onion in Italy, but the most known and appreciated is the Cipolla Rossa di Tropea Calabria PGI, which refers to the local ecotypes of Tondo Piatta (early ripening), Mezza Campana (mid-season) and Allungata (late ripening). The production area is located on the Tyrrhenian coast of northern Calabria and includes several municipalities in the provinces of Cosenza, Catanzaro and Vibo Valentia. It counts on a chain of 20 thousand tonnes of certified production with a consumer turnover of 60 million euros. Therefore, mechanical damage, which depends on several factors such as harvesting and post-harvest conditions, accelerates the deterioration of the product, besides damaging it aesthetically. Our research proposal focuses on interpretive techniques based on deep learning to identify deteriorating spring onions by distinguishing them from healthy ones.

Tropea Red Onion PGI: A Case Study on Impact Damage Using Artificial Intelligence / Bernardi, B., Papuzzo, G., Neri, A., Cilea, I., Sbaglia, M., Benalia, S., Abenavoli, L.M.M.. - 586:(2025), pp. 481-488. (International Mid-Term Conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024 ita 2024) [10.1007/978-3-031-84212-2_59].

Tropea Red Onion PGI: A Case Study on Impact Damage Using Artificial Intelligence

Bernardi B.;Neri A.;Cilea I.;Sbaglia M.;Benalia S.;Abenavoli L. M. M.
2025-01-01

Abstract

There are many varieties of onion in Italy, but the most known and appreciated is the Cipolla Rossa di Tropea Calabria PGI, which refers to the local ecotypes of Tondo Piatta (early ripening), Mezza Campana (mid-season) and Allungata (late ripening). The production area is located on the Tyrrhenian coast of northern Calabria and includes several municipalities in the provinces of Cosenza, Catanzaro and Vibo Valentia. It counts on a chain of 20 thousand tonnes of certified production with a consumer turnover of 60 million euros. Therefore, mechanical damage, which depends on several factors such as harvesting and post-harvest conditions, accelerates the deterioration of the product, besides damaging it aesthetically. Our research proposal focuses on interpretive techniques based on deep learning to identify deteriorating spring onions by distinguishing them from healthy ones.
2025
13-apr-2025
Inglese
Luigi Sartori, Paolo Tarolli, Lorenzo Guerrini, Giulia Zuecco, Andrea Pezzuolo
Lecture Notes in Civil Engineering
International Mid-Term Conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024
586
481
488
8
9783031842115
9783031842122
https://link.springer.com/chapter/10.1007/978-3-031-84212-2_59
Springer Science and Business Media Deutschland GmbH
2024
ita
Convolutional Neural Networks
Impacts
Spring Onion
Texture
YOLO8
No
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Bernardi, B.; Papuzzo, G.; Neri, A.; Cilea, I.; Sbaglia, M.; Benalia, S.; Abenavoli, L. M. M.
273
Tropea Red Onion PGI: A Case Study on Impact Damage Using Artificial Intelligence / Bernardi, B., Papuzzo, G., Neri, A., Cilea, I., Sbaglia, M., Benalia, S., Abenavoli, L.M.M.. - 586:(2025), pp. 481-488. (International Mid-Term Conference of the Italian Association of Agricultural Engineering, MID-TERM AIIA 2024 ita 2024) [10.1007/978-3-031-84212-2_59].
7
reserved
info:eu-repo/semantics/conferenceObject
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/157227
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