Author:

Keywords:

Science & Technology, Life Sciences & Biomedicine, Technology, Agriculture, Multidisciplinary, Computer Science, Interdisciplinary Applications, Agriculture, Computer Science, Precision Livestock Farming, Broiler breeder, Weighing, Image processing, 07 Agricultural and Veterinary Sciences, 08 Information and Computing Sciences, 09 Engineering, Agronomy & Agriculture, 30 Agricultural, veterinary and food sciences, 40 Engineering, 46 Information and computing sciences

Abstract:

The body weight of breeding broiler chickens (broiler breeders) is an important control variable used to optimize the amount, quality and fertility of the eggs being laid. In modern breeding barns, the population of animals generally supports a female:male ratio of 10:1, wherein males and females each receive their own food supply. To control this supply the broiler breeders are weighed using automatic electronic platform weighers and the weights are then classified into cockerel and hen categories using theoretical growth curves. However, due to the non-uniform growth of the animals and replacement (called spiking) of cockerals this classification is not always correct, causing a risk of under/over feeding of the population. To overcome this challenge, in this study we have developed a system that integrates the electronic platform weigher with the low-cost 3D Kinect camera was employed to separate weigh male and female broiler breeders under commercial conditions. A novel image processing algorithm is proposed. The algorithm first constructed the Height Accumulating Image (HAI) using depth image to locate the region of interest (ROI) where a broiler breeder jumps onto the weigher, then the comb size was calculated on RGB image as the gender classifying feature, and finally, an adaptive classification threshold was determined by the kernel density estimation using the recent days comb size measurements. The results showed that enough measurements can be obtained for estimating overall weight expectation with the average acceptance rate of 77.32%. Based on these accepted measurements, the accuracy, sensitivity, precision, and specificity were 99.7%, 98.82%, 100% and 100% respectively.