Identification de la brUlure de congElation sur la surface du saumon congelE en utilisant l'imagerie hyperspectrale et la vision par ordinateur combinEe avec l'algorithme d'apprentissage automatique
Identification of freezer burn on frozen salmon surface using hyperspectral imaging and computer vision combined with machine learning algorithm
Abstract This study explored the potential of computer vision system (CVS) and hyperspectral imaging (HSI) technique covering spectral range of 900–1700 nm for identifying freezer burnt salmon fillets after frozen storage. Local binary pattern (LBP) descriptor was applied for the RGB image classification. Reflectance spectra were obtained from various positions surface and pretreated using the standard normal variate (SNV) transformation. TreeBagger classifier was used to build classification models for recognition and authentication of the freezer burnt flesh. The results suggested that hyperspectral discrimination performed much better than CVS with the correct classification rate (CCR) of 0.905 in validation and CCR of 0.945 in cross-validation. The effective wavelengths were selected based upon the feature importance in the TreeBagger model and the corresponding optimized model yielded CCR of 0.914 in validation and 0.978 in cross-validation. Overall, the outcome suggested the capability of HSI for rapid categorization of damaged regions on frozen salmon. Highlights HSI and CVS were used to identify freezer burn in salmon fillets. TreeBagger classifier was applied to build classification models. HSI presented better classification performance than CVS. Simplified models were built with good results presented. The visualization of prediction map in salmon fillets was obtained.
Atlantic salmon . Hyperspectral imaging . Computer vision . TreeBagger classifier . Machine learning . Saumon atlantique . Imagerie hyperspectrale . Vision par ordinateur . Classificateur TreeBagger . Apprentissage automatique.
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