zahra roudbari

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 significant 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 propagation 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.