奶牛隐性乳房炎便携式计算机视觉快速检测系统设计与试验

为解决奶牛隐性乳房炎难以防治的问题,构建了一种基于计算机视觉技术的快速检测系统。通过电脑与USB摄像头采集牛奶p H测试纸图像,提出了一种融合颜色特征与形态学处理的分割方法,分割化学反应区并获取RGB值,使用Foss5000牛乳体细胞分析仪得到牛奶体细胞实测值,采取幂回归法建立RGB值与牛奶体细胞数的预测模型,并基于Android技术开发了便携式移动终端设备。牛场实测20组数据试验结果显示,牛奶体细胞数估测值与实测值相关系数为0.970,估测平均相对误差为3.67%,标准差为1.88%。系统估测牛奶体细胞数较为准确,可用于奶牛隐性乳房炎快速检测。

Design and experiment of rapid detection system of cow subclinical mastitis based on portable computer vision technology

Abstract: With the enhancement of living conditions, the demand for milk is increasing rapidly, the quality of milk is paid more and more attention, and the improvement of the quality of milk has already become an important issue. However, subclinical mastitis in dairy cows is the most dangerous and costly disease which is difficult to control in dairy farm. In recent years, about 1/3 cows of the world are suffering from mastitis, especially subclinical mastitis in dairy cattle. Among them, the incidence of subclinical mastitis is 40%-80% in China, which is seriously harmful to the healthy development of dairy industry. In order to solve the problem of rapid detection of subclinical mastitis in dairy cows, a fast test system based on the computer vision technology of subclinical mastitis was proposed in this paper. Firstly, 25 dairy cows were selected randomly in the experiment, including 5 dairy cows with recessive mastitis, 5 dairy cows with severe mastitis and other 15 healthy dairy cows. Each cow has 4 breasts, so there were 100 sets of data in total. The Foss 5 000 milk somatic cell counts detector was used to obtain the number of somatic cells per sample. At the same time, the samples were dropped on the pH test paper, whose images were collected by USB (Universal Serial Bus) camera connected with the computer. The collected milk pH test paper images were changed into 500 × 500 pixels, and transformed from RGB (red, green, blue) color space to HSV (hue, saturation, value) color space. According to the color characteristics of the pH test paper, the threshold value was selected and the collected images were binarized. On the other hand, the segmented image was processed by morphological processing to remove the segmentation error and edge burr. Finally, the segmentation results were achieved by fusing the 2 results. Linear regression, power regression, quadratic regression, and principal component regression were used to establish estimation models using 75 sets of data. Those models were compared using the remaining 25 sets of data. The power regression of the principal component had a higher correlation coefficient, a lower standard error, and the highest determination coefficient (R2) of 0.970. System function and user interface were designed based on Android programming technology. The second experiment was carried out in the cattle farm to validate the favorable model by using the designed mobile terminal equipment which was connected with the USB camera. Using the 20 sets of data to validate the model, the correlation coefficient of the estimated milk somatic cell counts and the measurement value was 0.970, the estimated average relative error was 3.67%, and the standard deviation was 1.88%. The established estimation model of milk somatic cell counts using R and G indices estimated the milk somatic cell counts better than the model using only one index and the model combining 3 indices. Through the model comparison using the 100 sets of data and the validation in the real farm, the detection system of milk somatic cell count is more accurate, and can be used for the rapid detection of subclinical mastitis in dairy cows.