Curl Size and Pelt Color Determination of Zandi Lambs Using Image Processing and Artificial Neural Network

Subject Areas : Camel

م.  خجسته کی ¹ (Department of Animal Science Research, Qom Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Qom, Iran)
ع.ا.  اسلمی نژاد ² (Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran)
ع.ر.  جعفری اروری ³ (Department of Animal Science, Qom Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Qom, Iran)

Received: 2018-04-21 Accepted : 2018-08-15 Published : 2019-03-01

Keywords: image processing, Artificial Neural Network, Zandi sheep, pelt quality,

Abstract :

In this study, a method based on using image processing and artificial neural network is introduced to determine pelt color and curl size of newborn lambs in Zandi sheep. The data was collected from 300 newborn lambs reared in the Zandi sheep breeding centre of Khojir, Tehran. Primarily, curl size and pelt color of new born lambs was recorded by experienced appraisers, and at the same time, several digital images were captured from the lateral side of each lamb. The features related to curl size and pelt color of lambs were extracted from digital images using image processing tools (IPT) of MATLAB software. To determining the pelt color, to classifying the pelts for curl size, and to estimating the curl size of pelt, three artificial neural networks were designed. The pelt color of the lambs was determined using an artificial neural network with a precision of 100%. The accuracy of the neural network which trained to classify the pelts on their curl size was 94.87%. The accuracy of the third neural network to estimate the curl size of pelts was 98.44%. The correlation between the curl size estimated using the artificial neural network and the curl size which measured by appraisers was 96.4% (P<0.01). The results of this study showed that there is a potential to use artificial intelligence as a substitute for human assessments in the recording of pelt traits.

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