Detection of Abalone Freshness Based on Smart Phone Image and Deep Learning
TL;DR
The results demonstrate that smartphone imaging combined with deep learning provides a rapid, low-cost, and nondestructive approach for abalone freshness assessment and has potential for digital quality monitoring in shellfish cold chains.
Abstract
Rapid and nondestructive freshness evaluation of abalone is important for quality control during cold-chain distribution, yet conventional chemical and microbiological methods are destructive and labor-intensive. In this study, a smartphone image-based deep learning strategy was developed for abalone freshness classification under refrigerated storage. Abalone samples stored at 4 °C were imaged daily under natural light, and freshness labels were assigned according to total volatile basic nitrogen (TVB-N) measurements. A total of 1867 images were used to develop binary classification models, and a transfer learning-based ResNet50 model was further interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM). TVB-N increased progressively during storage and exceeded the spoilage threshold on day 5 (15.63 ± 0.43 mg/100 g), which was used to define fresh (days 1–4) and spoiled (days 5–7) classes. Among the evaluated architectures, ResNet50 achieved the best overall performance, with a validation accuracy of 0.9611, precision of 0.9649, recall of 0.9091, and F1-score of 0.9362. On the test set, the model correctly classified 522 fresh and 220 spoiled images, yielding an overall accuracy of 96.11%. Grad-CAM visualization showed that the model mainly focused on the abalone body and marginal contour, indicating that predictions were driven by intrinsic appearance changes rather than background interference. These results demonstrate that smartphone imaging combined with deep learning provides a rapid, low-cost, and nondestructive approach for abalone freshness assessment and has potential for digital quality monitoring in shellfish cold chains.