Publication
Shelf-life management and ripening assessment of ‘hass’ avocado (persea americana) using deep learning approaches
| dc.contributor.author | Xavier, Pedro | |
| dc.contributor.author | Rodrigues, Pedro Miguel | |
| dc.contributor.author | Silva, Cristina L. M. | |
| dc.date.accessioned | 2024-04-17T10:32:38Z | |
| dc.date.available | 2024-04-17T10:32:38Z | |
| dc.date.issued | 2024-04-10 | |
| dc.description.abstract | Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behaviour, render avocados susceptible to significant loss and waste. To enhance the monitoring of ‘Hass’ avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit’s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labelled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.3390/foods13081150 | pt_PT |
| dc.identifier.eid | 85191354840 | |
| dc.identifier.issn | 2304-8158 | |
| dc.identifier.pmid | 38672823 | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/44647 | |
| dc.identifier.wos | 001211279700001 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Convolutional neural network | pt_PT |
| dc.subject | Fruit ripening | pt_PT |
| dc.subject | Shelf-life tracking | pt_PT |
| dc.subject | Post-harvest handling | pt_PT |
| dc.subject | Supply chain management | pt_PT |
| dc.title | Shelf-life management and ripening assessment of ‘hass’ avocado (persea americana) using deep learning approaches | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 8 | pt_PT |
| oaire.citation.title | Foods | pt_PT |
| oaire.citation.volume | 13 | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |