Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application

Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by...

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Veröffentlicht in:European food research & technology Jg. 250; H. 7; S. 1919 - 1932
Hauptverfasser: Yildiz, Muslume Beyza, Yasin, Elham Tahsin, Koklu, Murat
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024
Springer Nature B.V
Schlagworte:
Agriculture
Algorithms
Analytical Chemistry
Applications programs
Artificial intelligence
Artificial neural networks
Biotechnology
Chemistry
Chemistry and Materials Science
Classification
data collection
Deep learning
Essential nutrients
eyes
Fish
Food Science
Forestry
Freshness
Gills
Learning algorithms
Machine learning
Mobile computing
Neural networks
Nutrients
Original Paper
Protein sources
Proteins
smell
Deep learning
Feature extraction
Fish freshness
Classification
Machine learning
Fisheye
ISSN:1438-2377, 1438-2385
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Zusammenfassung:Fish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by examining its eyes, smell, skin, and gills. In this study, artificial intelligence techniques are employed to assess fish freshness. The author’s objective is to evaluate the freshness of fish by analyzing its eye characteristics. To achieve this, we have developed a combination of deep and machine learning models that accurately classify the freshness of fish. Furthermore, an application that utilizes both deep learning and machine learning, to instantly detect the freshness of any given fish sample was created. Two deep learning algorithms (SqueezeNet, and VGG19) were implemented to extract features from image data. Additionally, five machine learning models to classify the freshness levels of fish samples were applied. Machine learning models include (k-NN, RF, SVM, LR, and ANN). Based on the results, it can be inferred that employing the VGG19 model for feature selection in conjunction with an Artificial Neural Network (ANN) for classification yields the most favorable success rate of 77.3% for the FFE dataset. Graphical Abstract
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ISSN:1438-2377
1438-2385
DOI:10.1007/s00217-024-04493-0