Recognition of Mushroom Using Deep Convolutional Neural Networks Based on Attention Mechanisms

Abstract

Macro-fungi, usually called mushrooms, play a significant role in the ecosystem and share ubiquitous ecological niches in nature for their huge populations of over a million species. However, traditional classification approaches for mushrooms required professional taxonomic knowledge, and thus significant financial investment hinders its development with only ca. 20,000 species found now and only general applications employed such as poisonous mushroom identification. In this study, we creatively proposed an approach for automatic mushroom image recognition based on a deep convolutional neural network (DCNN). Attention mechanisms were combined with an efficient lightweight MobileNetV3 backbone network to achieve high performance on the mushroom image classification task. Our models achieve the highest 81.92% test accuracy on the public mushroom image dataset and 70.73% test accuracy on the local mushroom image dataset. Moreover, self-attention-based transformers are compared with lightweight DCNNs implementing attention mechanisms but do not achieve satisfying performance either on public or local datasets, which highlights the advantages of DCNNs for fine-grained biological image recognition. The proposed approach has demonstrated great potential for real-time and automatic mushroom image processing and the proposed automatic procedure will be complementary and a useful reference to traditional mushroom classification.