Wild Mushroom Classification Based on Improved MobileViT Deep Learning

Abstract

Wild mushrooms are not only tasty but also rich in nutritional value, but it is difficult for non-specialists to distinguish poisonous wild mushrooms accurately. Given the frequent occurrence of wild mushroom poisoning, we propose a new multidimensional feature fusion attention network (M-ViT) combining convolutional networks (ConvNets) and attention networks to compensate for the deficiency of pure ConvNets and pure attention networks. First, we introduced an attention mechanism Squeeze and Excitation (SE) module in the MobilenetV2 (MV2) structure of the network to enhance the representation of picture channels. Then, we designed a Multidimension Attention module (MDA) to guide the network to thoroughly learn and utilize local and global features through short connections. Moreover, using the Atrous Spatial Pyramid Pooling (ASPP) module to obtain longer distance relations, we fused the model features from different layers, and used the obtained joint features for wild mushroom classification. We validated the model on two datasets, mushroom and MO106, and the results showed that M-ViT performed the best on the two test datasets, with accurate dimensions of 96.21% and 91.83%, respectively. We compared the performance of our method with that of more advanced ConvNets and attention networks (Transformer), and our method achieved good results.