Nondestructive evaluation of fish meat using ultrasound signals and machine learning methods
| Autor: | Chihiro Saeki, Shinta Nakano, Makoto Nakamura, Kazuhiro Tokunaga, Shinichi Taniguchi, Hiromitsu Ohta |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Computer science business.industry 010604 marine biology & hydrobiology Ultrasound ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 04 agricultural and veterinary sciences Aquatic Science Texture (music) Machine learning computer.software_genre 01 natural sciences Frequency spectrum ComputingMethodologies_PATTERNRECOGNITION Histogram Nondestructive testing 040102 fisheries 0401 agriculture forestry and fisheries Fish Radial basis function Artificial intelligence business computer Bag of features ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Aquacultural Engineering. 89:102052 |
| ISSN: | 0144-8609 |
| DOI: | 10.1016/j.aquaeng.2020.102052 |
| Popis: | In this study, we propose a new method for the nondestructive measurement of the fat content and texture of fish meat using machine learning and the bag of features approach. We employed two machine learning methods, that is, a self-organizing map (SOM) and radial basis function (RBF) network. The SOM was applied to symbolize the pattern of the frequency spectrum extracted from ultrasound signals and to generate key features for the bag of features technique. The RBF network was applied to estimate the fat content and texture of fish meat from the bag of features histogram. We verified the accuracy of the fat content and texture estimations given by the proposed method through a series of experiments. The results showed that the fat content and texture of fish meat was estimated more accurately using the proposed method than by the conventional approach. |
| Databáze: | OpenAIRE |
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