Body Condition Score (BCS) Segmentation and Classification in Dairy Cows using R-CNN Deep Learning Architecture
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Tarih
2019
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info:eu-repo/semantics/openAccess
Özet
Body condition score (BCS) is based on scoring of dairy cattle from 1 to 5 according to the appearance of animals. BCS is a subjectivemethod based on assessing of subcutaneous fat thickness on the regions in back, waist and coccyx regions in cattle and the bone spursin the pelvic region by visual inspection and palpation method. BSC of animals in among the most important indicator of whether theneeds of animals are met in livestock enterprises. In general, BCS values are determined by a method based on expert knowledge anddetermined by observation. If the animal is above or below the desired BCS, at this stage, diseases resulting from metabolic problems,low yield or animal losses may occur. With the regular control of this situation, the profitability of the enterprise may increase withhealthier animals. For this purpose, in this study, it is aimed to segment the required regions and to classify the segmented regions inorder to perform BCS. Images taken from dairy cattle were trained with the R-CNN architecture used in object detection applications,which are among the Convolutional Neural Networks (CNN) architectures. Of the 184 images, 75% (138) were used for training and25% (46) were used for testing. During the training phase, the regions where BSC could be conducted from the raw images were labeledand these regions were learned. Then, the segmentation of the correct regions from the new images to the system was tested. Pre-trainednetworks were utilized to increase system success. For the classification of the segmented regions, the CNN network trained withAlexNet architecture was used. When the overall success of the system was evaluated, the AlexNet network correctly segmented 40 ofthe 46 raw test images, and the AlexNet CNN network correctly classified 28 of them and provided 60.86% overall success. The VGG16network correctly segmented 42 of the 46 raw test images, and the AlexNet CNN network correctly classified 30 of them, achieving65.21% overall success On the other hand, The VGG19 network correctly segmented 43 of the 46 raw test images, and the AlexNetCNN network correctly classified 31 of them, achieving 67.39% overall success.
Açıklama
Anahtar Kelimeler
Veterinerlik
Kaynak
Avrupa Bilim ve Teknoloji Dergisi
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0
Sayı
17
