Multispectral Imaging for Automated Fish Quality Grading
| Autor: | Sujeewa Ariyawansha, Dhananjaya Jayasundara, Sanjaya Herath, Roshan Godaliyadda, Neranjan Senarath, Lakshitha Ramanayake, Parakrama Ekanayake, Vijitha Herath |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Contextual image classification Computer science business.industry Multispectral image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Quadratic classifier Linear discriminant analysis Convolutional neural network Support vector machine ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business Grading (tumors) |
| Zdroj: | ICIIS |
| DOI: | 10.1109/iciis51140.2020.9342726 |
| Popis: | Fish grading is a vital process in the fisheries industry. In this paper, an algorithm is proposed utilizing multispectral imaging to automate fish grading. The images are obtained using an in-house developed Multispectral Imaging System. A Convolutional Neural Network (CNN) for image classification is utilized. From the CNN method, 93% accuracy was achieved. In addition to that, machine learning algorithms including Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) were performed on the preprocessed dataset for comparison purpose. |
| Databáze: | OpenAIRE |
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