Identification Of Tartary Buckwheat Seeds Based On Machine Vision

Tartary buckwheat is a kind of herbaceous plant with homology between medicines and food homology.Different varieties of buckwheat seeds contain different functional ingredients.According to different concerns,the selection of varieties is not the same,In order to avoid the doping between different varieties of buckwheat seeds.The quality of buckwheat seeds is getting higher and higher.In order to improve the identification efficiency of buckwheat seeds identification and at the same time reduce the cost of identification of buckwheat,this study proposes a method for the identification of tartary buckwheat seeds based on machine vision.Firstly,the preprocessing operation of the tartary data set makes it more suitable for neural network input,and then extracting features according to the improved convolution residual neural network convolution residual network presented in this study,and then the multi-classification support vector machine suitable for feature space characteristics is selected to classify the buckwheat cultivars.The main contents and conclusions of this study are as follows.(1)The pretreatment of tartary buckwheat datasets.The image of the given tartary buckwheat dataset is too high in dimension and cannot be directly used as input to the convolution residual network.A method for normalizing the seed image of buckwheat has been proposed for this problem.Firstly,the principal component analysis method is used to determine the angle of rotation of buckwheat,and then the buckwheat image is rotated according to the rotation angle so that the horizontal orientation is uniform.The background area of the buckwheat image is relatively large,in order to remove the extra background image,A tactical image clipping method based on target detection is proposed.(2)Proposed buckwheat seeds identification model.In order to allow neural networks to extract tartary buckwheat features more abundantly,multi-scale convolution kernels are used for each convolutional layer.In order to avoid the phenomena such as gradient disappearance or explosion in the network,the design of residual network is adopted in the network.The identity mapping is added.Therefore,the convolution residual neural network convolution residual network is proposed in this paper.Using tripletloss to update the parameters of the network,this method can make the characteristics of the type of tartary buckwheat more converge,and the characteristics of different types of tartary buckwheat are as far apart as possible.Experiments show that convolution residual network is 20 percentage points higher than the normal classification method.(3)Research on convolution residual network classification performance under different data sizes.The number of each type of buckwheat in the given dataset is small,it is unable to fully reflect the classification performance of convolution residual network.In addition,in order to increase the amount of data while preventing the occurrence of overfitting of the network,this paper adopts a data augmentation method for datasets.The final experiment shows that convolution residual network's classification performance is higher under large data volume.