Measuring fruit quality traits in olive through RGB imaging and artificial neural networks: opportunities and limitations-Reference-Cited by-同舟云学术

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Measuring fruit quality traits in olive through RGB imaging and artificial neural networks: opportunities and limitations

Published:2023-11-06 Issue: Volume: Page:685-688
ISSN:
Container-title:2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)
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Author:

Montanaro Giuseppe¹,Petrozza Angelo²,Rustioni Laura³,Cellini Francesco²,Carlomagno Antonio¹,Nuzzo Vitale¹

Affiliation:

1. University of Basilicata,DiCEM,Potenza,Italy

2. Centro Ricerche Metapontum Agrobios ALSIA Metaponto di Bernalda (MT),Italy

3. University of Salento,DiSTeBA,Lecce,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424027/10424015/10424226.pdf?arnumber=10424226

Reference12 articles.

1. Image-Based Assessment of Drought Response in Grapevines

2. Drought phenotyping in Vitis vinifera using RGB and NIR imaging

3. ANN-based method for olive Ripening Index automatic prediction

4. A critical review on the use of artificial neural networks in olive oil production, characterization and authentication

5. Antioxidant Activity and Anthocyanin Contents in Olives (cv Cellina di Nardò) during Ripening and after Fermentation

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