Comparing the Accuracy of Artificial Neural Networks in Estimating the Weight of Cobb, Ross, and Arbor Acres Chicks using Video Image Processing Technology

Abstract

This study aimed to compare the accuracy of artificial neural networks (ANNs) in estimating the weight of broilers using video image processing technology. A total number of 900 broiler chicks from three different strains (Ross 308, Cobb 500, and Arbor Acres) were fed on commercial diets and reared under standard situations for 42 days. Thirty male and female chicks from each strain were weighed randomly using digital scales every day while simultaneously filmed from top view using a Xenon camera (2MP 1080IP lens). In image processing, digital images initially were extracted from films and then each image was processed using GUI of MATLAB software. Sixteen morphological features extracted from images that significantly correlated with the chicks' weight, were used as inputs of the artificial neural network, and multilayer perceptron ANN was trained to predict the weight of chickens of each strain via an error propagation algorithm. The procedure was the same for all three strains. The accuracy of ANN models to predict the weight of chicks were 98.4% (with an average error of 7.9 g), 99.54% (with an average error of 0.37 g), and 99.67% (with an average error of 2 g) for Ross, Cobb, and Arbor Acres strains, respectively. In conclusion, a comprehensive intelligent model can be designed based on artificial neural networks and video image processing technology to estimate the weight of broiler chickens regardless of their strain type.