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汤勇,洪琪,王巧华,祝志慧.基于血线纹理特征和GA-BP神经网络的鸡种蛋性别鉴定[J].华中农业大学学报,2018,37(06):130-135
基于血线纹理特征和GA-BP神经网络的鸡种蛋性别鉴定
Sex identification of chicken eggs based on blood line texture features and GA-BP neural network
投稿时间:2017-12-20  
DOI:
中文关键词:  鸡种蛋; 性别鉴定  血线; BP神经网络; 遗传算法; 无损检测
英文关键词:chicken egg  sex identification  blood line  BP neural network  genetic algorithm  nondestructive testing
基金项目:中央高校基本科研业务费专项(2662017PY057);公益性行业(农业)科研专项(201303084)
作者单位E-mail
汤勇 华中农业大学工学院武汉 430070 zctangyong@126.com 
洪琪 华中农业大学工学院武汉 430070  
王巧华 华中农业大学工学院武汉 430070
.农业部长江中下游农业装备重点实验室武汉 430070 
 
祝志慧 华中农业大学工学院武汉 430070
.农业部长江中下游农业装备重点实验室武汉 430070 
zzh@mail.hzau.edu.cn 
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中文摘要:
      为了对鸡种蛋孵化早期胚胎性别进行鉴别,构建机器视觉图像采集系统,在LED光源下获取186枚种蛋孵化第4天的图像。采用对鸡种蛋图像进行分量提取、去背景化和二值化等预处理方法,利用自适应直方图均衡化、高低帽变换增强图像,通过迭代阈值分割和“与”运算凸显血线纹理。运用差分计盒法、灰度共生矩阵法、灰度直方图统计法和几何法提取图像的11维特征参数,并构建鸡种蛋胚胎性别识别的BP模型(back propagation neural network,BPNN),利用遗传算法(genetic algorithm,GA)优化BP神经网络的初始权值和阈值。试验结果表明,GA-BP模型的训练集识别综合准确率为99.73%,预测集识别综合准确率为82.80%。
英文摘要:
      A machine vision image acquisition system was constructed to obtain 186 eggs hatching the 4 d image under LED light source to identify the early embryonic sex of chicken eggs. The preprocesses including component extraction,debackgrounding and binarization of egg image were carried out,followed by the using adaptive histogram equalization,top-hat and bottom-hat to enhance the image. Threshold segmentation of iterative add “and” operation was used to highlight the blood line texture. The 11-dimensional feature parameters of the image were extracted with the method of difference box,gray level co-occurrence matrix,gray histogram and geometric. When a BP (back propagation neural network,BPNN) model of chicken egg embryo sex identification was built,genetic algorithm (genetic algorithm,GA) was used to optimize BP neural network initial weights and thresholds initial. The results showed that the comprehensive accuracy of the training set of the GA-BP model was 99.73%,and the comprehensive accuracy of prediction set was 82.80%.
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