GAN-Augmented Deep Learning Model for Automated Fruit Ripeness Classification

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ID: 312659
2025
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Abstract
Manual fruit harvesting is costly, labor-intensive, and difficult to scale. We present a vision-based system for automated fruit-ripeness classification that jointly predicts fruit type and ripeness stage as an eight-class task {mango, strawberry, tomato, sweet pepper}×{ripe, unripe}. To mitigate limited and imbalanced field data, we train class-conditioned GANs to synthesize additional images and build an augmented training set (3,988 real images → 57,339 total). Synthetic samples are quality-controlled using automated filters and human review and are used only in training; validation and test splits contain real images exclusively. We benchmark three classifiers, DenseNet-201, ResNet-50, and a compact CNN, using a unified pipeline with standard preprocessing and on-the-fly augmentation. DenseNet-201 achieves the best generalization, reaching 99.41% training accuracy, 98.01% validation accuracy, and 95.7% accuracy on the held-out real-image test set, outperforming ResNet-50 and the CNN baseline on recall, precision, and F1 score. The results indicate that targeted generative augmentation improves robustness to variations in viewpoint, partial occlusion, and illumination, enabling reliable ripeness assessment from in-orchard imagery. The proposed pipeline provides a reproducible foundation for integrating ripeness classification into robotic harvesting workflows and can be extended to additional crops, sensing modalities, and on-device inference.
Reference Key
imported_1777055854_69ebb86e542df Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Salman Qadri
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
10.56979/1001/2025/1119
URL
https://jcbi.org/index.php/Main/article/view/1119
Keywords Keywords not found

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