A Hybrid Particle Size Algorithm for Classification of Hygienic Fruit and Vegetable Images Based on Convolution Neural Network from Health Perspective

摘要

In order to improve the clarity of selection of hygienic fruit and vegetable images, a hybrid granularity classification algorithm for fruit and vegetable images based on convolutional neural network is proposed. The edges of fruit and vegetable images are detected, and the fruit and vegetable images are preprocessed under the convolutional neural network. The fruit and vegetable images are sampled in the form of equal intervals, the mixed noise in the fruit and vegetable images is judged, and the window pixels are marked. Finally, the wavelet threshold algorithm is used to filter the noise of the fruit and vegetable image, and the wavelet of the effective low-frequency signal is reconstructed combined with the convolution neural network the mixed noise filtered fruit and vegetable image is obtained by using the threshold high frequency signal coefficient and the threshold high frequency signal coefficient. Experimental results show that the algorithm has high definition, good denoising effect, and high measurement accuracy.