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Development of convolutional neural network models for evaluation of body condition scores of Holstein Friesian crossbred cows

Indian Agricultural Research Journals

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Title Development of convolutional neural network models for evaluation of body condition scores of Holstein Friesian crossbred cows
 
Creator Shriramulu
Amaladass Pushpadass, Heartwin
Eljeeva Emerald Franklin, Magdaline
Kanagaraj, Manimala
Sakthivel, Jeyakumar
A. Kataktalware, Mukund
Muniandy, Sivaram
P. Kerekoppa , Ramesha
 
Description Body condition scoring (BCS) is an efficient tool to monitor the nutritional status of cows by subjective assessment of the amount of fat or stored energy in them. This method of scoring is cumbersome, laborious and inefficient because it takes more time to score due to increasing number of animals in modern dairy farms, besides involving high cost. Therefore, this study proposed a system based on convolutional neural network (CNN) models to automate BCS of cows by image analysis. The digitally-captured images were processed using GIMP software to subtract the background from the captured images of cows. The background-subtracted images were used to detect the edges and contours using fuzzy logic edge detection method in Matlab software. Finally, the images with body contours and edges were employed as input dataset for the development of CNN models. The image dataset was classified into two groups based on the incremental BCS system of 0.25 (CNN model 1) and 0.5 (CNN model 2). The classification accuracy of the first model for 0.25 and 0.50 error ranges was 61.41% and 80.31%, respectively. Similarly, the second model achieved classification accuracy of 81.45% and 93.54%, for the same error ranges. The CNN model 2 was relatively better as compared to the first model owing to the wider BCS range of classification.
 
Publisher Indian Dairy Association, New Delhi, India
 
Date 2024-06-24
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
 
Identifier https://epubs.icar.org.in/index.php/IJDS/article/view/142670
 
Source Indian Journal of Dairy Science; Vol. 77 No. 3 (2024): May-June 2024
2454-2172
0019-5146
 
Language eng
 
Relation https://epubs.icar.org.in/index.php/IJDS/article/view/142670/54883