Adaptive identification method of grape downy mildew based on quantile of boundary sample
Aiming at the problem of difficulty to determine the threshold of grape downy mildew lesion tissue image,an adaptive threshold determination method based on quantiles of boundary samples was proposed.The boundary of diseased spot was identified by Gaussian filtering,and the threshold of diseased spot was determined by 50%quantiles of boundary sample.Then Monte Carlo method was used to estimate the proportion of diseased spots by random sampling method.The results show that compared with other threshold determination methods,the proposed method can adaptively obtain the grayscale threshold of the lesion,with a recognition accuracy of 92.2%,which is significantly higher than other threshold determination methods.Compared with the traditional machine learning methods,the recognition accuracy of this method is higher than that of BP neural network,convolutional neural network,and support vector machine,slightly lower than 94.3%of the VGG16 model and 96.26%of the ResNet50 model.However,the calculation time is 1.410 s,which is much faster than 5.588 s and 20.317 s of the VGG16 model and ResNet50 model,indicating that this method can achieve high accuracy in a shorter running time.
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