• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Scientific reports. >Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques
【24h】

Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques

机译：基于计算机视觉的常规机器学习和深层学习技术，识别模具殖民地对无与伦比的稻田

获取原文
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

To investigate the potential of conventional and deep learning techniques to recognize the species and distribution of mould in unhulled paddy, samples were inoculated and cultivated with five species of mould, and sample images were captured. The mould recognition methods were built using support vector machine (SVM), back-propagation neural network (BPNN), convolutional neural network (CNN), and deep belief network (DBN) models. An accuracy rate of 100% was achieved by using the DBN model to identify the mould species in the sample images based on selected colour-histogram parameters, followed by the SVM and BPNN models. A pitch segmentation recognition method combined with different classification models was developed to recognize the mould colony areas in the image. The accuracy rates of the SVM and CNN models for pitch classification were approximately 90% and were higher than those of the BPNN and DBN models. The CNN and DBN models showed quicker calculation speeds for recognizing all of the pitches segmented from a single sample image. Finally, an efficient uniform CNN pitch classification model for all five types of sample images was built. This work compares multiple classification models and provides feasible recognition methods for mouldy unhulled paddy recognition.
机译：为了探讨常规和深度学习技术的潜力，以识别出在未渗透的稻谷中的模具的物种和分布，用五种模具接种并培养样品，并捕获样品图像。模具识别方法是使用支持向量机（SVM），后传播神经网络（BPNN），卷积神经网络（CNN）和深度信仰网络（DBN）模型构建的模具识别方法。通过使用DBN模型实现了100％的精度率，以基于所选择的颜色直方图参数识别样品图像中的模具种类，然后是SVM和BPNN模型。开发了一种与不同分类模型相结合的桨距分割识别方法以识别图像中的模殖区。间距分类的SVM和CNN模型的精度率大约为90％，高于BPNN和DBN模型的速度。 CNN和DBN模型显示了更快的计算速度，用于识别从单个样本图像分段的所有间距。最后，建立了所有五种样本图像的有效统一的CNN间距分类模型。这项工作比较了多种分类模型，为FLOSID UPLULLED稻谷识别提供了可行的识别方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号