artificial neural network

In many countries of the world, the camel has played an important role in the lives of people in arid and semi-arid regions in terms of providing milk and meat products. Based on phenotypic characteristics, the camel has been adapted to the deserts from physiological, anatomical, and behavioral points of view. Due to limited water resources, the south Khorasan province of Iran has not been an appropriate geographical region for a great number of agricultural products. Weighing plays an important role in the management of camel breeding to adjust the nutritional needs and also the annual evaluation of the animals. One of the main challenges of camel breeding is the difficulty of recording and the lack of records due to the wild nature and also the large size, especially in adults. Due to the many difficulties and risks, camel breeders usually use various potential alternative tools such as apparent estimation or weighing tape to estimate the weight of camels at different ages. For farm animals, significant correlations between body measurements and body weight can be used as a tool to estimate the weight of animals through a mathematical equation. The main objective of this study was to compare the accuracy of artificial neural network (ANN) and multiple linear regression model (MLR) in estimating the body weight of a humped camel.
In the present study, the data of a total number of 177 one-humped camels in four groups  (including 1. 63 adult female Pakistani camels aged between 9 and 12 years, 2. 21 adult female Baluchi camels aged between 9 and 12 years, 3. 93 male and female camels less than 2 years of age of Pakistani and Baluchi breeds, and 4. total camels) collected at the South Khorasan camel breeding station in 2019 were used. Morphological characteristics of camels include: neck length, neck girth, hand length, foot length, shoulder height to the ground, hump height to the ground, hip height to the ground, chest girth, chest width, abdomen girth, hip width, tail length, breast height, and breast girth were measured. After measuring the body dimensions, the evaluated camels were weighed using a 1000 kg digital scale. To estimate the weight of camels from their body dimensions, the data were analyzed using the MLR model and ANN with the nnet package in R software. For ANN analysis, 80% of the data were considered for network training and 20% for testing. The accuracy of MLR and ANN for camel body weight estimation was compared using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).
The results of the MLR model showed that body dimensions of shoulder height to the ground, chest girth, abdominal girth, neck girth, and hand length had a significant effect on body weight (P<0.05). In the ANN model, chest girth, abdominal girth, and shoulder height to the ground were the most important variables in estimating the body weight of camels of the whole population. The MLR and ANN models had acceptable accuracy in weight estimation. However, compared to the MLR model, the ANN model had a higher R2 and a lower error in estimating the weight of camels. In the first group, R2 and RMSE were found to be 0.996 and 6.852, respectively, for ANN while the corresponding values were 0.979 and 22.955, respectively, for MLR.  In the second group, R2 and RMSE were found to be 0.995 and 3.525, respectively, for ANN while the corresponding values were 0.989 and 5.377, respectively, for MLR.  In the third group, R2 and RMSE were found to be 0.896 and 13.959, respectively, for ANN while the corresponding values were 0.849 and 17.549, respectively, for MLR. In the fourth group, R2 and RMSE were found to be 0.929 and 20.248, respectively, for ANN while the corresponding values were 0.903 and 38.505, respectively, for MLR. The results showed that ANN is more accurate compared to MLR in predicting the body weight of camels. 
In terms of goodness of fit (including R2 and RMSE), the results of the present research suggest that both MLR and ANN methods have high acceptable accuracy for predicting body weight in camels. ANN was more suitable compared to MLR, suggesting that it could be used to predict camel body weight. Furthermore, grouping the camels by age and breed could also lead to higher precision and lower prediction error.

In this study artificial neural network (ANN) used to predict milk test day records at 4th, 5th, 10th months of lactation duration and 305-day milk yield in Holstein dairy cows. Primary data source was consisting of 274025 milk production records of 7201 primiparuse to fourth birth Holstein cows, from two herd. Final source of data obtained from milk production records was consist of 87980 monthly milk test day records in 8798 rows which each row contains number of animal, herd, age, lactation, month of production, first to tenth monthly milk production records and 305-day milk yield. From the total of data, 50% was considered for neural network training, 20% for validation and 30% for testing. A multilayer perceptron (MLP) network with back propagation of error learning mechanism (BP) was used through different artificial neural network (ANN) structures to predict milk production. In order to optimize artificial neural network (ANN) structure three activation functions (hyperbolic tangent axon, sigmoid axon or linear hyperbolic tangent axon) and three back propagation algorithms viz. momentum, conjugate gradient (CG) and Leven-berg–Marquardt (LM) Training algorithms used in the hidden layer as well as in the output layer. Coefficient of determination, root of mean square error and mean absolute error were used to compare algorithms. In prediction of milk production of 4th and 5th monthly test day records, LM algorithm with sigmoid axon activation functions and LM Training algorithm, with hyperbolic tangent Axon functions had the best performance between network structures respectively. In these net work structures R2 were highest (0.725 and 0.642 respectively), RMSEs were lowest (4.785 and 5.345 respectively) and MAEs were lowest (3.715and 4.057 respectively). In prediction of 10th monthly test day milk production through three or four monthly test day records, obtained from the same lactation period, none of the structures had ability to predict milk production successfully. In prediction of 305-day milk yield, LM algorithm and hyperbolic tangent activation function had the best prediction through 3 test day records and R2, RMSE and MAE as performance criteria were 0.799, 984.14 and 790.21 respectively. Also the same structure of the network had the best performance to predict 305-day milk yield through four or five initial test day records and performance criteria, Coefficient of determination, root of mean square error and mean absolute error were 0.856, 850.98 and 653.33 respectively, in ANN with four test day record as input variables and 0.904, 706.59 and 548.69 respectively, in ANN with five test day record as input variables, respectively. The artificial neural network designed in this study was able to predict the milk production of animals in the fourth month of lactation with a correlation coefficient of 0.84. On the other hand, the designed neural network was able to predict the total milk production of the animal in a lactation period of 305 days with appropriate accuracy. So that the correlation coefficients in using the first three, four and five monthly records of livestock for prediction were 0.89, 0.92 and 0.95 respectively.

Artificial neural networks (ANN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on animal science. The objective of this study was to prediction of breeding values of weaning weight in Markhoz goats based on the Mixed Model Equation (MME) and Artificial Neural Networks (ANNs). Quality control and calculation of descriptive statistics was performed using the GLM procedure of the SAS statistical package. The pedigree file included 5541 kids produced by 261 bucks and 1616 does. In the first step, genetic evaluations and Best Linear Unbiased Prediction (BLUP) of breeding values for weaning weight was computed with the mixed model equations, animal model by DMU program. Later, unique dataset was introduced to the ANN models by the R statistical program. A variety of models including, multilayer perceptron (MLP), radial basis function (RBF) and Support Vector Regression (SVR) were used to build the neural models. The artificial neural networks were trained and several networks (different hidden layers and nodes/ neurons) were evaluated. In artificial neural networks, the data were randomly divided to two parts (75% training and 25% for test/validation). Best architecture was selected according to the mean square of error and correlation. Correlation between true breeding value (BVs predicted by MME) and estimated breeding value (BVs predicted by ANNs) for MLP, RBF and SVR models were 0.72, 0.49 and 0.73, respectively. Analysis of farm data showed that the MLP and SVR models have higher performance than RBF for prediction of breeding values or ranking of individuals.

In order to compare the performance of neural network with some mathematical functions for predicting of lactation curve of Iranian Holstein dairy cows, a total of 1,085,525 milk test day records from first-parity dairy cows calved during 1983-2012 were used. Fitting the lactation curve was performed by brnn package (for neural network) and also by some mathematical functions (including Wood, Wilmink, Ali-Schaeffer and Pollott-Gootwine) using R software based upon average milk yield, fat and protein percentage test day records. The criteria of AIC, BIC, RMSE and adjusted R2 were utilized to evaluate goodness of fit. The results showed that the Bayesian neural network (brnn) had a better fit than mathematical functions in describing the standard curve shape of Iranian Holstein dairy cows. Among the mathematical functions used for milk yield, Wilmink model had a better fit while for milk fat percentage and milk protein percentage, Ali-Schaeffer model showed a better fit performance. Therefore, it could be suggested that brnn is an appropriate option to be applied to fit the lactation curve of Iranian Holstein dairy cows.

This study was conducted to investigate the effects of mixer added fat, crude protein and conditioning temperature on the pellet durability index, and electrical energy consumption during feed production using computational modeling tools. A total of 192 broiler feed samples with different levels of mixer added fat and crude protein in feed components and different conditioning temperatures to determine the pellet durability index, modified pellet durability index and electrical energy consumption during feed production were used. Multiple linear regression and artificial neural network were used to analyze data. Both models had the ability to predict the value of the pellet durability index, modified pellet durability index and the electrical energy consumption during feed production; but the prediction accuracy of the artificial neural network model was higher than that of the multiple linear regression model for all three outputs. Optimization was done using the artificial neural network model, and in these calculations, in order to achieve the highest possible level of pellet physical quality and the lowest possible level of electrical energy consumption, the crude protein amount was 20-20.5% and the conditioning temperature was predicted to be 85 C. However, the amount of fat was predicted to be 1% for the highest amount of pellet physical quality and 4% for the lowest amount of electrical energy consumption during production. In practical conditions, this model can help in more accurate prediction of electricity consumption and the quality of produced feed in order to achieve the optimal situation in feed production factories.

This study was conducted for predicting performance traits of the whole production period of Arian broiler breeder via its initial performances using artificial neural network (ANN). The input variables of modeling were house, number of hens in the house, weeks of production, body weight at 20-24 weeks of age and outputs of the model were body weight, egg number, egg mass, egg weight and feed intake at the 25 to 47 weeks of age. The used ANN model for prediction of body weight had 5 inputs, 5 neurons at 1st hidden layer, 2 neurons at 2nd hidden layer and 1 output, thus we write it as 5-5-3-1. Similarly, the optimized ANN model structure for feed intake, egg number, egg weight and egg mass were 7-7-4-1, 8-8-4-1, 7-7-3-1 and 7-7-3-1, respectively. R2 of adequate models for BW, FI, EN, EW and EM were 0.991, 0.998, 0.989, 0.993 and 0.996, and Root Mean Square Error were 1.55, 0.992, 0.266, 3.838 and 0.506, respectively. The results of the study shown that architecture and the specification of the neural networks such as inputs, outputs, number of neurons and number of hidden layers can affect the performance of the ANN model. The results indicated the possibility of predicting whole production period of Arian broiler breeder using early stage production records.

In the management of camel breeding, weightlifting plays a decisive role in grouping livestock, regulating nutritional needs and also annual evaluation of animals. Due to the many difficulties and risks, camel owners usually prefer alternative methods such as using apparent estimates or weight -meter to estimate the weight of camels. Since the accuracy of mathematical models in estimating the weight of camels is not equal, so the present research was conducted to compare the accuracy of estimating artificial neural network and multiple linear regression model in estimating the weight of dromedary camels from their body dimensions. For this purpose, 26 camels with 203 records were used from a private farm for one year. Weighing and determining the body dimensions of camels (body length, shoulder height, back height, hump height to the ground, chest and abdomen girth) were measured monthly. To estimate the weight of camels from their body dimensions, the data were analyzed using multiple linear regression model and artificial neural network. The weight of camels on their body dimensions was estimated with accuracy of 0.94 and 0.99, respectively, using multivariate linear regression model and artificial neural network model. The weight of camels on their body dimensions was estimated with accuracy of 0.94 and 0.99, respectively, using multivariate linear regression model and artificial neural network model. The results of this research showed that the artificial neural network has the proper ability to estimate the weight of camels based on their body dimensions and can replace conventional regression methods.

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.

The aim of this research was to compare the efficiency and performance of the advanced artificial neural network method with the principal component analysis method in discriminating different horse breeds. In this study, two methods of perceptron neural network (Olden) and the principal component analysis (PCA), were used to identify a subset of SNP markers with the highest breed discrimination potential and to investigate how to assign animals to their breed groups. The results showed that the network method (Olden),  is able to separate all the 37 horse breeds with a small subset of SNP markers (8,000 markers) with a same capability to all genomic markers (98% accuracy). The PCA selection method was only able to identify and separate breeds with diverse geographical originations. According to the obtained results, the PCA method is not error-free and depends upon changes and modifications to run on genomic data. The results of this study provide practical approaches in the design of economic arrays for discriminating the different horse breeds.

This study was conducted to evaluate the effects of different dietary levels of Hemp seed (HS) (Cannabis sativa-L.) on growth parameters of broiler chickens was estimated using Gompertz and Artificial neural network models.

In this study, 192 male broiler chicks (1 d old-Ross 308) were randomly assigned to a completely randomized design with 4 dietary treatments: control (without HS), 2.5, 5 and 7.5% HS in 4 replications (12 birds/pen). The chickens had freely accessed to drinking water and fed ad-libitum. To estimate growth parameters, cumulative body weight of birds was fitted to Gompertz model.

The results showed that different dietary levels of Hemp seed had no significant effect on growth parameters of broiler chickens (p>0.05). But growth rate at first to fifth weeks affected by different dietary levels of Hemp Seed (p<0.05). Results of comparison of models showed that non-linear Gompertz model had higher R2, and lower MSE, MAD, MAPE and bias compared with artificial neural network, that had better estimate of weight of broiler chickens.

The results of this study showed that different levels of Hemp seed diets had a significant effect on growth parameters of broilers including relative growth rate, live weight at maturity, turning point of growth curve and body weight at turning point of growth curve. Growth rate in the first to fifth weeks of broiler breeding period was significantly affected by the addition of hemp seed diets. On the other hand, the results of this study showed that the Gampertz model was able to estimate the 42-day-old weight of broilers more accurately than the artificial neural network model.

In order to compare the two methods of artificial neural networks (ANN) and multiple linear regression to predict yearling weight used records of 736 Raieni goat. Effects and variables influencing on yearling weight were; animal sex, type of birth, birth year, birth season, herd and characters related to birth weight, three months weight, six months weight and nine months weight. In order to process of data using a neural network model, applied the 3 MLP with a different number of input type. Data modeling were created by neural network software STATISTICA 10. In multiple regression data analyzed using software SAS 9.1.3 Protable with stepwise regression method and suitable model were selected according to R2, RMSE and bias. The results in this research showed a better accuracy of the ANN systems than regression methods (R-square value made by neural networks MLP1 to MLP3 equal to 0.998, 0.998 and 0.997 and RMSE value were estimated 0.96, 0.97 and 1.22, respectively) for predicting yearling weight of these animals.

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.

In this research, the artificial neural networks (ANNs) and multi-layered non-fuzzy inductive inference system (ANFIS) were used to model the output energy in dairy and beef cattle farms. For this purpose, according to Cochran's relation, 105 beef and dairy farms were randomly selected from five townships which were the main producers of this sector in Mazandaran province from 2016-2017. Using the energy balance of inputs and outputs, the input and output energy averages in beef production farms were calculated to be 16994.76 and 3449.85 MJcow-1 and for dairy production farms were equal to 100100 and 58277 MJcow-1, respectively. Also, ER (Energy Ratio), EP (Energy Productivity), SE (Special Energy) and NE (Net Energy) indices for dairy production farms were 0.58, 0.08 KgMJ-1, 12.5 MJKg-1 and -41825.93 MJcow-1, respectively and for beef production farms were calculated as 0.2, 0.02 KgMJ-1, 50 MJKg-1and 13544.91 MJcow-1, respectively. Using the results of statistical analysis of the data, modeling of the output energy for each unit of input energy was performed by two above methods (ANNs and ANFIS). The results showed that the model of nervous- fuzzy inference comparative multi-layered system with the correlation coefficient of 0.9899 for steer farms and 0.9933 for dairy farms, had better performance and accuracy than that of the artificial neural network with the correlation coefficient of 0.8118 and the structure of 6-16-1 for beef farms and correlation coefficient of 0.9837 and the structure of 5-12-1 for dairy farms.

Precise prediction of milk yield is essential for management and modeling of farmer’s income in analysis of cost-benefit. Such that, accurate prediction of future records can decrease recording time. Early estimation of bull breeding value using partly records of parity can lower generation interval and increase selection intensity and genetic progress. Linear model is the most commonly used modeling method in research on different field of science. Artificial Neural Network (ANN) is based on artificial intelligent that uses working principles of human brain. Ease of application and power to model complex functions and relationships is factor of wide use of Artificial Neural Network. Revolutionized use of artificial neural network modeling in different aspects of science in the last two decades, is indication of successful application of this powerful technique to solve wide range of problems in different scientific issues. Therefore, object of current research is prediction milk yield of different parity milk production of dairy cattle using production of first recorded parity and artificial neural networks modeling. Furthermore, results of artificial neural network model compared with linear model. In current research, two sequential records of 2460 dairy cattle of a herd were investigated. Pedigree of used data set contained 2517 individuals. Data divided into two sub data of training and testing, to fitting Artificial Neural Network model. Artificial Network model learned the relationship between output and inputs of training data set. Adequacy parameters of the model investigated using model predicted outputs of testing data set and original outputs of the data. Network structure with the beast adequacy parameters were used for Artificial Neural Network model. Finally, linear model was fitted and compared with artificial neural network model. The best structure of Neural Network had 8 inputs, 4 neuron at first hidden layer, 2 neuron at second hidden layer and output of milk production that inputs were breeding value of average milk yield of first recorded parity, parity, sire group, age at first registered parturition, number of records for each parity and mean, minimum and maximum of recorded days in milk for each parity. The used artificial neural network model, predicted the parity milk production with RMSE and R2 of 7.94 and 0.625, respectively. R2 and RMSE of considered linear model was 0.39 and 26.63, respectively. The applied model of artificial neural network appropriately predicted the subsequent parity production using precedent parity data. This research indicated that use of artificial network model can be beneficial for decreasing recording period for dairy cattle genetic evaluation specially in sire evaluation and will decrease generation interval. The results showed that incomplete data can be used for genetic evaluation using artificial neural network model. Comparison of the results with past reports indicated that use of effective inputs for milk production can increase accuracy and precision of the ANN model.

This research was conducted to investigate the feasibility of estimating the weight of broiler chicks using machine vision technology. 600 Ross broiler chicks were reared under standard conditions for a 42-day period. At selected intervals (7 days), 60 birds from a total of 600 chicks were randomly selected and weighed individually using the appropriate scale. At the same times, digital images were captured individually and in groups 2, 3 and 4 of birds. The digital images were initially preprocessed and the necessary changes were made on the photos and required features were extracted from images by designing an appropriate algorithm, and these features were used to design the neural network to estimate the body weight of chicks. The correlation coefficient between the extracted features of digital images including the Major axis length, Minor axis length, Bonding box, Convex Area, Filled area, Perimeter and Area of the image with live weight of the chicks were 0/92, 0/93, 0/53, 0/99, 0/99, 0/94, and 0/99 respectively (p <0.01). A Multilayer perceptron neural network, which was trained with back propagation learning algorithm, containing 22 neurons in the input layer, 20 neurons in the mid layer and one neuron in the output layer presented the highest accuracy(99%) to estimate the weight of broiler chicks at different ages. The results of this study showed that there is a possibility of using image processing and artificial neural network as an appropriate and efficient tool to estimate the weight of broiler chicks during the breeding period.

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, the relationships between live body weight at slaughter, carcass characteristics (weights of hot carcass, cold carcass, cold half-carcass, lean meat, fat, bone and fat-tail) and body measurements (body length, chest girth, hip width and shoulder width) were investigated in Lori-Bakhtiari sheep. Two methods of linear regression and artificial neural network (ANN) in prediction of live weight and carcass characteristics were compared.
The data of 58 male lambs were collected at the Breeding Station of Lori-Bakhtiari sheep in Shahrekord city of Chaharmahal and Bakhtiari province, Iran. The lambs were weaned at 90 ± 5 days of age and were fattened in three groups of 60, 80 and 100 days. At the end of fattening period, body measurements and after slaughtering and skinning, all carcass characteristics were measured. A total of 696 records were used for prediction of carcass characteristics by using regression models and ANN. The possibility of predicting carcass characteristics and live weight at slaughter through body measurements was evaluated by choosing the most suitable regression model based on the value of the determination coefficient. The selective regression models also were fitted by artificial neural network then; these two methods were compared based on the value of the determination coefficient and the mean square errors.
The estimated phenotypic correlation coefficients between carcass characteristics and live weight at slaughter were generally positive and relatively high (0.58 to 0.99). The phenotypic correlation coefficients between carcass characteristics and body measurements were estimated between 0.29 and 0.69.
Between all body measurements, the highest correlations are observed between chest girth and carcass characteristics and live body weight. Based on relatively high phenotypic correlation coefficients in this research, prediction of carcass characteristics and live body weight can be possible with high accuracy by using body measurements. The results show that, to prediction of live body weight of Lori-Bakhtiari male lambs, body length, chest girth and hip width must be included in the regression model to obtain an accuracy of 79 percent. The regression equation including live body weight and heart girth could predict hot carcass and cold carcass weights with an accuracy of 97 and 96 percent, respectively. Live body weight alone explains the 94 and 72 percent of the variation of half-carcass and fat-tail weight, respectively. Moreover, the results show that the accuracy of ANN model for prediction of some carcass traits was more than regression models.
Based on results of this research, prediction of carcass characteristics and live weight is possible with relatively high accuracy by using some phenotypic body measurements. Also, results indicate that the artificial neural network technique was much better capable of predicting weight and carcass traits in Lori-Bakhtiari sheep compared to linear regression equations. The results of this study can be useful due to the importance of carcass traits in determination of genetic potential and regulation of animal breeding programs for high meat production.

Calving interval is sought to be substantially affected by management and environmental effects. Using intellectual algorithms of machine learning methods to investigate complex systems are growing and these algorithms could be assumed as right approach to analysis dairy cattle industry data. Artificial neural networks which are part of artificial intelligence, gain from these algorithms. The objective of this study was to find the association between productive and reproductive traits with calving interval over different course of inseminations. It was assumed that extracting this association may improve the management of this trait.
In this research, the productive and reproductive traits from FOKA, an agriculture and animal husbandry, associated to Isfahan Vahdat Cooperative was used. The records were calving interval (day), the length of dry off period (day), number of insemination, total milk production (kg), total fat production (kg), total protein production (kg), adjusted milk production (kg), adjusted fat production (kg) and adjusted protein production (kg). The data were due to 15465 cows in which their parturition date spanned between 1368 and 1393. The data dimensions were reduced using elimination of high level of correlation among variables and principal component analysis before undertaking artificial neural network modeling. For each insemination, we learned a neural network. We used coefficient of determination and root mean square error to investigate the efficiency of neural network. The linear and nonlinear relationship among input variables with calving interval was measured using maximal information criterion.
The similar values of coefficient of determination and root mean square error due to neural network in different insemination obtained which would indicated that there is almost identical associations between calving interval input variables. Higher values of root mean square error obtained in neural network which learned based upon principal component analysis of independent data than elimination one. Therefore, it was concluded that elimination approach is suitable choice in this context. The maximal information criterion showed almost an identical association between calving interval and input variables over different inseminations. In this way, days in milk and milk production showed high amount of correlation with calving interval.
The results indicated that coefficient of determination and root mean square error of different inseminations was similar. Therefore, it is hardly expected that the association between calving interval and independent variables to be different over different inseminations. Based upon these results, it was elucidated that independent variables used in this study could fairly adequately predict the calving interval. Also, we could say that management – environmental conditions over different inseminations had minuscule effect on the associations of calving interval with independent variables.

The aim of this study was to compare different nonlinear and linear regression models and artificial neural network (ANN) for fitting of growth curve in Lori– Bakhtiari sheep breed. Six nonlinear regression models of Negative Exponential, Brody, von Bertalanffy, Gompertz, Logistic and Richards, and two linear regression models of second and third degree polynomial functions along with ANN were used. In total 29517 body weight records of 6320 lambs collected from birth to yearling were analyzed. The data were collected at the Breeding Station of Lori-Bakhtiari sheep in Shahrekord, Iran. The comparison of the models was carried out using coefficient of determination (R2), error mean square (MSE) mean absolute deviation (MAD) and mean absolute percentage error (MAPE) values. All models investigated in the current study fitted the growth data well in Lori– Bakhtiari sheep, according to different goodness of fit criteria. The results indicated that ANN model generated better growth curve fitting of Lori-Bakhtiari sheep than linear and nonlinear growth models and could be used an as alternative method for growth modeling. Regarding the whole models, the artificial neural network was found to be statistically most appropriate model followed by Brody, third degree polynomial, second degree polynomial, Von Bertalanffy, Gompertz, Richards, Logistic and negative exponential growth models, respectively. However, non-linear growth models used to describe the growth will be applicable than the linear models and artificial neural networks. The nonlinear growth models can summarize the growth phenomena in terms of several parameters, with biological interpretation. Among the nonlinear and linear models, the Brody and third degree polynomial functions were better than other models. Negative correlations between the A and k parameters were obtained in all six nonlinear growth models in this study indicated that the sheep with smaller mature weight will be maturing faster. The analysis of variance on the Brody growth curve parameters showed that year of birth and sex significantly influenced (P<0.01) all growth curve parameters. Age of dam had only a significant effect (P<0.05) on k value and did not contribute to differences in A and B values. Type of birth had significant effect (P<0.01) in the B and k values and did not influence on the A value. The results of this study suggest that the most appropriate growth model of Brody can help in the determination of management problems, regulation of feeding programs, and determination of optimum slaughtering age at the LoriBakhtiari sheep breeding station.