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首页> 美国卫生研究院文献>Scientific Reports >Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques
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Recognition of Mould Colony on Unhulled Paddy Based on Computer Vision using Conventional Machine-learning and Deep Learning Techniques

机译：基于机器视觉的传统机器学习和深度学习技术对去壳稻霉菌菌落的识别

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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：为了研究常规和深度学习技术识别未去壳稻谷中霉菌的种类和分布的潜力，将样品接种并用五种霉菌进行培养，并捕获样品图像。模具识别方法是使用支持向量机（SVM），反向传播神经网络（BPNN），卷积神经网络（CNN）和深度置信网络（DBN）模型建立的。通过使用DBN模型基于选定的颜色直方图参数，然后是SVM和BPNN模型，在样本图像中识别霉菌种类，可以达到100％的准确率。提出了一种结合不同分类模型的音高分割识别方法，以识别图像中的霉菌菌落区域。支持向量机和CNN模型进行音高分类的准确率约为90％，高于BPNN和DBN模型。 CNN和DBN模型显示出更快的计算速度，可以识别从单个样本图像分割的所有音高。最后，针对所有五种样本图像建立了有效的统一CNN音高分类模型。这项工作比较了多个分类模型，并为发霉的脱壳稻田识别提供了可行的识别方法。

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