Khojastehkey et al. Iranian Journal of Applied Animal Science (2020) 10(2), 333-340 333 Feasibility of Body Weight Estimation of Kalkoohi Camels Using Digital Image Processing INTRODUCTION In order to breed domestic animals, it is inevitable to record their productive traits. Increasing the accuracy in keeping records from domestic animals will increase the accuracy of the final assessment and, ultimately, will increase the ge- netic progress of breeding herds (Miller, 2010). Camel breeding is more difficult than other domestic animals due to the forceful nature of the animal and its breeding system. It is usually difficult and time consuming to approach a camel and keep the animal in good order to record its traits (Kuria et al. 2007). For example, weighing camels is difficult because of their large size and lack of proper handling. Presently, cam- els are mostly weighed through truck loading and in groups. In order to weigh camels individually, we need cranes to put camels on the scale and unload them after weighing; therefore, providing such facilities in the natural habitat of camels is a difficult task. In addition, the animal’s move- ments might reduce the speed and accuracy of weighing The aim of this study was to investigate the possibility of estimating the weight of Kalkoohi camels using digital image processing. For this purpose, Kalkoohi camels were weighed monthly on a private farm for one year. On the day of weighing, digital images were taken from all camels from their lateral side. These digital images were processed in MATLAB software environment and the required numerical features of each image including different morphological features were extracted. Among all extracted features, major axis length, minor axis length, the number of non-zero elements (NNZ) and equivalent diameter had a sig- nificant and high correlation with the weight of camels (P<0.01) and were considered as effective features in developing neural network. The multi-layer artificial neural network, which was trained by back propa- gation algorithm, was used to estimate the weight of camels based on their digital images. The accuracy of the final model in estimating the weight of Kalkoohi camels based on their image features was 99%. The correlation coefficient between the estimated weights by artificial neural network model and the actual weights of the camels was 98%, and the deviation of estimated weights from the measured weight of camels was 2.21 kg. The results of this study revealed that digital image processing technology has a good potential to estimate the weight of Kalkoohi camels, and this method could be a good alternative to weigh camels using a scale. KEY WORDS image processing, Kalkoohi camels, weight estimation. M. Khojastehkey 1* , M. Kalantar Neyestanaki 1 , Z. Roudbari 2 , H. Sadeghipanah 3 , H. Javaheri 3 and A.R. Aghashahi 3 1 Department of Animal Science, Qom Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Qom, Iran 2 Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran 3 Department of Animal Science, Animal Science Research Institute of Iran (ASRI), Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran Received on: 20 Jun 2019 Revised on: 19 Aug 2019 Accepted on: 31 Aug 2019 Online Published on: Jun 2020 *Correspondence E‐mail: m.khojastehkey@areeo.ir © 2010 Copyright by Islamic Azad University, Rasht Branch, Rasht, Iran Online version is available on: www.ijas.ir Research Article