Application of Deep Learning for Automated Peach Classification: A Study Based on ResNet Architectures Article Swipe
Flossi Puma-Ttito
,
Carlos Guerrero-Méndez
,
Daniela López-Betancur
,
Tonatiuh Saucedo-Anaya
,
Rafael Castaneda-Diaz
,
Luis Martinez-Ytuza
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.61467/2007.1558.2025.v16i3.1141
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.61467/2007.1558.2025.v16i3.1141
This study evaluates the performance of various ResNet architectures for classifying peaches as “healthy” or “damaged”. A dataset of 3 370 images was used, with data-augmentation techniques applied to enrich the training set. Transfer learning was performed using pre-trained ResNet models, with stochastic gradient descent (SGD) adopted as the optimisation algorithm. Performance was assessed using accuracy, precision, recall and F1 score. ResNet-50 emerged as the most effective architecture, achieving a mean accuracy of 95.96 % and outperforming other models, including ResNet-18, ResNet-34, ResNet-101 and ResNet-152. The results demonstrate the potential of deep-learning techniques to improve peach-sorting processes, thereby reducing post-harvest losses and enhancing quality control in the agricultural sector.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.61467/2007.1558.2025.v16i3.1141
- https://ijcopi.org/ojs/article/download/1141/399
- OA Status
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412379255
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412379255Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.61467/2007.1558.2025.v16i3.1141Digital Object Identifier
- Title
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Application of Deep Learning for Automated Peach Classification: A Study Based on ResNet ArchitecturesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-14Full publication date if available
- Authors
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Flossi Puma-Ttito, Carlos Guerrero-Méndez, Daniela López-Betancur, Tonatiuh Saucedo-Anaya, Rafael Castaneda-Diaz, Luis Martinez-YtuzaList of authors in order
- Landing page
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https://doi.org/10.61467/2007.1558.2025.v16i3.1141Publisher landing page
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https://ijcopi.org/ojs/article/download/1141/399Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
- OA URL
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https://ijcopi.org/ojs/article/download/1141/399Direct OA link when available
- Concepts
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Residual neural network, Deep learning, Artificial intelligence, Computer science, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Performance | 51 |
| abstract_inverted_index.ResNet-152. | 84 |
| abstract_inverted_index.classifying | 10 |
| abstract_inverted_index.demonstrate | 87 |
| abstract_inverted_index.performance | 4 |
| abstract_inverted_index.pre-trained | 38 |
| abstract_inverted_index.agricultural | 107 |
| abstract_inverted_index.optimisation | 49 |
| abstract_inverted_index.post-harvest | 99 |
| abstract_inverted_index.architecture, | 67 |
| abstract_inverted_index.architectures | 8 |
| abstract_inverted_index.deep-learning | 91 |
| abstract_inverted_index.outperforming | 76 |
| abstract_inverted_index.peach-sorting | 95 |
| abstract_inverted_index.“healthy” | 13 |
| abstract_inverted_index.“damaged”. | 15 |
| abstract_inverted_index.data-augmentation | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.19807238 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |