基于嵌入式系统与机器视觉的上孵前无精蛋识别系统

为避免未受精蛋的孵化,将计算机图像处理技术与嵌入式技术有机结合,提出了无精蛋识别系统设计方案.该系统首先通过计算机视觉技术采集种蛋图像,之后运用数字图像处理技术完成种蛋图像区域不变性参数(圆度、复杂性、伸长度、球状性、长短轴比和平均变动系数)的提取,最后采用已训练的遗传神经网络实现无精蛋识别.实验结果表明:该系统简单便捷,易于实现,对种蛋的600枚非实验样本的识别精度为99.3％,满足上孵前剔除无精蛋的要求.

Unfertilized Eggs Verification System before Hatching Based on Embedded System and Machine Vision

In order to research the problem of unfertilized eggs verification before hatching, combined the technology of digital image processing and embedded technology, a design scheme of unfertilized eggs filtering system was put forward based on embedded computer. At first, the images of eggs that were collected by using computer vision technology were applied, and then the invariance parameters of image area were extracted, such as circularity, complexity, elongation, globularity, ratio of major to minor axis and average alteration coefficient. At last, identification of unfertilized eggs based on trained genetic algorithm neural network was realized. The test result showed that the system was convenient and easy to realize. Using 600 non-samples of the hatching eggs as verification samples and the result showed the identification rate could reach to 99.3%, satisfied the requirement of eliminating the unfertilized eggs before hatching.