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EN
The problem of segmenting the cross-section through the longissimus muscle in beef carcasses with computer vision methods was investigated. The available data were 111 images of cross-sections coming from 28 cows (typically four images per cow). Training data were the pixels of the muscles, marked manually. The AlexNet deep convolutional neural network was used as the classifier, and single pixels were the classified objects. Each pixel was presented to the network together with its small circular neighbourhood, and with its context represented by the further neighbourhood, darkened by halving the image intensity. The average classification accuracy was 96%. The accuracy without darkening the context was found to be smaller, with a small but statistically significant difference. The segmentation of the longissimus muscle is the introductory stage for the next steps of assessing the quality of beef for the alimentary purposes.
EN
In this article we have presented genetic predispositions and the influence of numerous factors that occur during cattle fattening, transport and holding in slaughter plants, on their welfare and subsequently on the occurrence of defective meat, known in the literature as DFD (Dark, Firm, Dry) meat. Such meat is unwillingly bought by contemporary consumers because of its consistency and processing plants cannot intend it for production of durable processed meat due to its compliance to decay. To a great extent the occurrence of this meat defect is caused by people as a result of ignorance and incompliance of cattle welfare. It should also be remarked that there is a rising number of consumers interested in the place of origin of cattle and the way it was treated.
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