Research On Quality Grading Of Plateau Summer Vegetables Based On Deep Learning

With the progress and development of society,people pay more and more attention to the quality of vegetables,fruits and other daily necessities,and the demand has gradually increased,but due to various reasons such as the lack of standardization in the sales link and the weak awareness of vegetable quality grading in vegetable trading enterprises,the quality and price of vegetables cannot be equal,and high quality and price cannot be achieved.At present,plateau summer vegetables have become a pillar characteristic industry in Gansu Province,but they still use traditional manual sorting or ordinary mechanical sorting devices,which will not only consume a lot of manpower,material resources and costs,but also the quality of the vegetables produced cannot be guaranteed,resulting in negative impact.With the rising popularity of deep learning,the study of the quality grading method of plateau summer vegetables based on deep learning is also of great significance and wide application value for the sales of plateau summer vegetables in Gansu Province.Therefore,in view of the problems arising from the traditional grading method,this paper proposes a method to study the quality grading of plateau summer vegetables by deep learning,the main contents are as follows:(1)Build a dataset of plateau summer vegetables.In view of the current lack of plateau summer vegetable dataset,a set of plateau summer vegetable image acquisition device was established,and four vegetables,kale,baby cabbage,cauliflower and broccoli,were collected as the original data set,with a total of 2400 pictures.(2)Enrich the original dataset.In order to ensure that the collected plateau summer vegetable dataset can have a good grading effect during model training,this paper first coarsely segmented the image to eliminate background interference.Then,the data augmentation method is used to augment the original image to make it more conducive to the subsequent training of convolutional neural network models.(3)A multi-scale fusion CA-Ghost-Efficient Net model for the quality grading of plateau summer vegetables is proposed.Firstly,a lightweight model is constructed,the first convolutional layer in the Efficient Net network model is replaced by the Ghost layer,and at the same time,the CBAM lightweight attention module is embedded before the last layer of the network,which accelerates the training speed of the network model and allows the network to pay more attention to subtle features,detect and locate locally useful information,and make the analysis of similar species more accurate.Then,the attention mechanism SE module in the MBConv structure in the network is replaced with the CA module,so that the network can retain the long-term dependence of features and accurate location information at the same time.Finally,the optimization algorithm of the network is improved,and the RAdam algorithm is combined with the multi-scale fusion algorithm,so that the network can retain more features of the dataset image,and also avoid the phenomenon that the network falls into local optimum.In this paper,the improved Efficient Net model and classical neural model are trained on the preprocessed plateau summer vegetable dataset,and the ablation comparison experiment is carried out,and the experimental results show that the accuracy and number of model parameters of the proposed method are significantly better than other networks and unimproved networks,which proves the effectiveness and feasibility of the improved network model.