Application of Deep Learning for Automated Peach Classification: A Study Based on ResNet Architectures Article Swipe
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· 2025
· Open Access
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· 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.
Related Topics To Compare & Contrast
- 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
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412379255