Classification and adulteration degree of Maojian tea based on multi-scale and attention mechanism
In order to solve the problem that it is difficult for consumers to distinguish the varieties of Maojian tea and the degree of adulteration in their daily life,a network model based on multi-scale feature extraction and efficient channel attention mechanism is proposed.Based on DenseNet121,the multi-scale feature extraction structure is used to replace the original single convolution kernel,which enriches the information of the feature layer.Then,ECA-Net attention mechanism is introduced at the dense connection block of the model to enhance the transmission of effective feature information.Finally,the parameters of the model are optimized to improve its recognition performance.The results show that the improved MS-ECA-DenseNet121-C classification model achieves a recognition accuracy of 96.95%on the collected data sets with eight varieties of Maojian tea and their adulteration,which can effectively identify the authenticity of Maojian tea varieties.And the size of the improved model is only 27.3 MB,which is easy to deploy on the mobile phone and has certain application value in the field of tea recognition.
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