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.

育种肉鸡（种鸡）的体重是重要的控制变量，用于优化产蛋的数量，质量和繁殖力。在现代的畜舍中，动物种群通常以10：1的雌雄比例维持生存，其中雄性和雌性各自获得自己的食物供应。为了控制供应量，使用自动电子平台秤对肉鸡种鸡进行称重，然后使用理论生长曲线将其重量分为公鸡和母鸡类别。但是，由于动物的生长不均匀和鸡冠肉的替换（称为尖刺），这种分类并不总是正确的，从而导致人口饲喂不足/过量的风险。为了克服这一挑战，在这项研究中，我们开发了一种将电子平台秤与低成本3D Kinect相机集成在一起的系统，该系统用于在商业条件下分离雄性和雌性肉鸡繁殖者的体重。提出了一种新颖的图像处理算法。该算法首先使用深度图像构建高度累积图像（HAI），以定位肉鸡育种者跳到称重机上的感兴趣区域（ROI），然后在RGB图像上计算梳子大小作为性别分类特征，最后，自适应分类阈值是使用最近几天的梳子大小测量值通过籽粒密度估计确定的。结果表明，可以得到足够的测量值来估计总体重量期望值，平均接受率为77.32％。根据这些公认的度量，