  Molecular and morphological characters, as important parts of biological
taxonomy, are contradictory but need to be integrated. Organism's image
recognition and bioinformatics are emerging and hot problems nowadays but with
a gap between them. In this work, a multi-branching recognition framework
mediated by genetic information bridges this barrier, which establishes the
link between macro-morphology and micro-molecular information of mushrooms. The
novel multi-perspective structure is proposed to fuse the feature images from
three branching models, which significantly improves the accuracy of
recognition by about 10% and up to more than 90%. Further, genetic information
is implemented to the mushroom image recognition task by using genetic distance
embeddings as the representation space for predicting image distance and
species identification. Semantic overfitting of traditional classification
tasks and the granularity of fine-grained image recognition are also discussed
in depth for the first time. The generalizability of the model was investigated
in fine-grained scenarios using zero-shot learning tasks, which could predict
the taxonomic and evolutionary information of unseen samples. We presented the
first method to map images to DNA, namely used an encoder mapping image to
genetic distances, and then decoded DNA through a pre-trained decoder, where
the total test accuracy on 37 species for DNA prediction is 87.45%. This study
creates a novel recognition framework by systematically studying the mushroom
image recognition problem, bridging the gap between macroscopic biological
information and microscopic molecular information, which will provide a new
reference for intelligent biometrics in the future.
