Quality Grading of Dried Abalone Using an Optimized VGGNet
Source abstract
Abstract
As living standards have improved, consumer demand for high-quality dried abalone has increased. Traditional abalone grading is achieved through slice analysis (sampling analysis) combined with human experience. However, this method has several issues, including non-uniform grading standards, low detection accuracy, inconsistency between internal and external quality, and high loss rate. Therefore, we propose a deep-learning-aided approach leveraging X-ray images that can achieve efficient and non-destructive internal quality grading of dried abalone. To the best of our knowledge, this is the first work to use X-ray to image the internal structure of dried abalone. The work was divided into three phases. First, a database of X-ray images of dried abalone was constructed, containing 644 samples, and the relationship between the X-ray images and the internal quality of the dried abalone was analyzed. Second, the database was augmented by image rotation, image mirroring, and noise superposition. Subsequently, a model selection evaluation process was carried out. The evaluation results showed that, in a comparison with models such as VGG-16, MobileNet (Version 1.0), AlexNet, and Xception, VGG-19 demonstrated the best performance in the quality grading of dried abalone. Finally, a modified VGG-19 network based on the CBAM was proposed to classify the quality of dried abalone. The results show that the proposed quality grading method for dried abalone was effective, achieving a score of 95.14%, and outperformed the competitors, i.e., VGG-19 alone and VGG-19 with the squeeze-and-excitation block (SE) attention mechanism.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Meat and Animal Product Quality · Identification and Quantification in Food
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361005, China
College of Ocean and Earth Sciences, Xiamen University, Xiamen 361005, China
Fujian Key Laboratory of Genetics and Breeding of Marine Organisms, Xiamen University, Xiamen 361102, China
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen 361005, China
Key Laboratory of Underwater Acoustic Communication and Marine Information, Xiamen University, Xiamen 361005, China
Funding
Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM) · National Nature Science Foundation of China · National Funding Program for Postdoctoral Researchers · China Postdoctoral Science Foundation
References (29)
Show source-provided references
1. Real-time detection of water dynamics in abalone (Haliotis discus hannai Ino) during drying and rehydration processes assessed by LF-NMR and MRI10.1080/07373937.2017.1300807 ↗
2. High pressure impregnation-assisted drying of abalone (Haliotis rufescens) slices: Changes in protein conformation, thermal properties, and microstructure
3. Processing Optimization of Dried Abalone Based on Texture Characteristics and the Microstructure Comparison
4. Effect of boiling on texture of abalone muscles and its mechanism based on proteomic techniques10.1016/j.foodchem.2022.133014 ↗
5. Effect of Drying Methods to Nutritional Compositions of Abalone
6. Effect of different cooking methods on nutritional intake and different storage treatments on nutritional losses of abalone10.1016/j.foodchem.2022.132047 ↗
7. Interactive effects of diet and water temperature on the nutritional value, flavor, and sensory quality in hybrid abalone meat10.1016/j.aquaculture.2023.739241 ↗
8. X-ray Applications in Food and Agriculture: A Review
9. X-ray microtomography of materials
10. Review of medical imaging with emphasis on X-ray detectors, Nuclear Instruments Methods in Physics Research. Section A, Accelerators, Spectrometers10.1016/j.nima.2006.01.123 ↗
11. High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications10.1016/j.earscirev.2013.04.003 ↗
12. X-ray fluorescence application in food, feed, and agricultural science: A critical review
13. Low-density foreign body detection in food products using single-shot grid-based dark-field X-ray imaging10.1016/j.jfoodeng.2022.111189 ↗
14. Inline nondestructive internal disorder detection in pear fruit using explainable deep anomaly detection on X-ray images10.1016/j.compag.2022.106962 ↗
15. Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv.
16. Squeeze-and-Excitation Networks10.1109/TPAMI.2019.2913372 ↗
17. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module, Springer.10.1007/978-3-030-01234-2_1 ↗
18. Enhancing soybean classification with modified inception model: Atransfer learning approach10.3897/ejfa.2024.122928 ↗
19. Alkanan, M., and Gulzar, Y. (2024). Enhanced corn seed disease classification: Leveraging MobileNetV2 with feature augmentation and transfer learning. Front. Appl. Math. Stat., 9.10.3389/fams.2023.1320177 ↗
20. Gulzar, Y. (2023). Fruit image classification model based on MobileNetV2 with deep transfer learning technique. Sustainability, 15.10.3390/su15031906 ↗
21. Gao, Z. (2011). Study on Drying Technology and Product Characteristics of Abalone. [Master’s Thesis, Fujian Agriculture and Forestry University].
22. Toughness and Collagen Content of Abalone Muscles10.1271/bbb.57.6 ↗
23. Abalone gastropod of different parts and structure study
24. O’Keeffe, D. (2010). Denoising of Carpal Bones for Computerised Assessment of Bone Age, University of Canterbury, Department of Electrical Computer Engineering.
25. Howard, A.G., Zhu, M., and Chen, B. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv.
26. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
27. Hollet, F. (2016, January 17–21). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2017.195 ↗
28. A Review on the Attention Mechanism of Deep Learning10.1016/j.neucom.2021.03.091 ↗
29. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22–29). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.74 ↗