جستجوی مقالات مرتبط با کلیدواژه "پردازش تصویر" در نشریات گروه "علوم دام"

Due to weakness, the occurrence of unwanted errors, the impact of the environment and exposure to natural events, human always make mistakes in their diagnoses of the environment or different topics, so that different people 's perception of a single and unique event may be very different and be diverse. Nowadays, with the development of image processing technology, human beings try to evaluate the speed and accuracy of their evaluation and diagnosis about objects, plants and animals by using hardware and software facilities and by using the features extracted from images related to objects, plants and animals. To increase and therefore has created a new technology called image processing and has developed it in various dimensions.

In order to identify the best artificial neural network training algorithm for estimating the weight of Kurdi sheep using digital image processing, lambs and adult animals at the sheep breeding station of North Khorasan province were weighed using a scale. During the weighing, some digital images were taken from the side view of sheep using a digital camera by discerning fixed distance. Image processing steps and feature extraction from images of sheep were done using GUI of MATLAB (R2010a) software. Then, three types of artificial neural networks were trained using different types of educational procedure, including Levenberg Marquarth (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) Training algorithms. The extracted features from images were used as input and weight of sheep as output in the training steps of ANNs, and the accuracy of the ANN models in estimating the weight of sheep was compared.

As results, the accuracy of the trained ANNs with the three algorithms including SCG, BR and LM, in estimating the weight of sheep in the training phase was estimated to be 91.95, 94.74 and 94.94%, respectively. In the practical test, which was performed by presenting 20 images as a test to each ANN models, the trained ANNs with the SCG, BR and LM algorithm had 4.7%, 0.5% and 2.11% error in estimating the weight. The results showed that all three types of ANN training algorithms had acceptable accuracy to estimate the weight of sheep, meanwhile the accuracy of the artificial neural network trained with BR algorithm was better than the others.

The performance of the proposed method based on image processing and the use of artificial neural network is accurate enough to estimate the weight of Kurdi sheep. It had better performance. Based on the results of the present study, it is quite possible to develop applications based on the use of artificial intelligence to weigh domestic animals, and use of this technology is recommended in several cases where there is no quick and easy access to the scales.

Measuring the body dimensions in livestock is usually useful to predict the weight, grade and body score of animals. The shoulder height, chest circumference, chest depth, body length, forehead bone size, rump height, distance between eyes, ear length, and tail length are the most important body linear measurements in the livestock. Most of these dimensions are related to the live weight and some important traits of domestic animals. For example, the results of many studies indicated that the chest circumference, body length, pelvic width and shoulder height are the most appropriate and reliable parameters for estimating live weight of the animals. In recent studies, features of digital images have been used, in certain circumstances, to estimate body dimensions of domestic animals. The base of these measurements is the machine learning technology, and currently was tested on some livestock, such as dairy cattle. Therefore, this research was conducted to investigate the possibility of using machine vision technology in order to estimate body dimensions of one-humped camel.

This research was conducted on one-humped camel in a privative camel breeding herd at Qom province. The studied herd originally consisted of 9 adult mature camels, one an adult luck and 11 pedigree camels from 2 to 12 months old of age. In the following months, five baby camels were born in the herd. The body dimensions of all camels were monthly recorded, and in the same time, digital images were captured from camels regarding a constant distance (2 meters). In this study, a total of 203 biometric records of camels at different ages were measured. Each photo was first transferred to a computer, and some edits were made to improve it's quality. Twenty two morphological features from each image were extracted using defined functions of graphical user interface of MATLAB. The characteristics that were more relevant to the biometric measurement of camels were selected using Pearson correlation coefficient by SPSS software. The data mining process with the aim of discovering mathematical relationship between extracted features of digital images and body dimensions of camels was done using a feed forward neural network which was trained by the "back propagation algorithm" in MATLAB software.

Some extracted features including equivalent diameter, major axis length, minor axis length, bounding box, convex area, filled area, area ,perimeter and the number of non zero points in digital images had high and significant correlation (P<0.01) with body dimensions of camels. These features were used as effective input to design the artificial neural network. Accuracy of the artificial neural network models to estimate body length, shoulder height, and hip height of one-humped camels were 0.98, 0.96 and 0.96, respectively.

The use of image processing and artificial neural network or other data mining tools can be considered as an appropriate and accurate alternative to human assessments, and help to save the time and expense associated with the biometry of large livestock, especially camels.

This study introduces a method based on machine vision technology for weight estimation, shape index determination, and egg contamination detection. Primarily, different characteristics relevant to egg quality such as weight, length, wide, shape index and contamination statue of 76 eggs were recorded by experienced appraisers. At the same time, using a digital camera several photos were taken from each egg regarding fixed imaging distance and unique illumination. Then, the features relevant to egg weight, egg size and egg shell contamination were extracted from digital images using image processing tools (IPT) of MATLAB software. Four artificial neural networks were designed for egg contamination classification, egg weight, and egg length and egg wide estimation respectively using MATLAB software. The first neural network was trained to detect egg contamination without error, and in the test phase, the eggs contamination was recognized with accuracy of 95% by neural network. Next neural networks were trained to estimate the egg weight, egg length and egg wide with accuracy of 98.55, 99.4 and 98.49 % respectively. In the test phase, the correlations between egg weight, egg length and egg wide with those which estimated by artificial neural networks were equal to 96.6 , 97.31 and 98% respectively (P

In this study, a method based on using image processing and artificial neural network is introduced to determine curl type and pelt size of new born lambs in Zandi sheep. The data was collected from luxury pelt collection belong to the Zandi sheep breeding center Khojir, Tehran, Iran. Primarily, curl type and dimensions were determined by experienced appraisers for some pelts. Then, some digital images were captured from different curl types on the pelts and from different pelt dimensions. The features related to curl type determination and pelt size estimation were extracted from digital images using image processing tools (IPT) of MATLAB software. Two different Artificial Neural Networks were designed for classification of curl types and estimation of pelt size, independently. To classifying the curl types, a neural network was trained with the accuracy of 100%, and in the test phase it could classify the 7 curl types with the accuracy of near 95%. The accuracy of pelt size estimation was near 99% for the second tested neural network. There was a significant correlation (97.15%) between true and ANN estimated pelt sizes. The results indicated that there is a potential of using artificial intelligence as a replacement for the human sense, to determine pelt quality in the new born lambs of pelt sheep.