DNAVMM: A DNA and Visual Fusion Model for Multimodal Species Classification in Ecology

DNAVMM is an innovative multimodal deep learning framework that pioneers the integration of DNA barcode data and visual images to achieve more accurate automatic species identification and classification. It aims to address the issues of traditional species identification relying on expert experience, being time-consuming and labor-intensive, and supports ecology and biodiversity research.

Global biodiversity is under threat, and accurate and rapid species identification is crucial for ecological monitoring and conservation. Existing automatic species identification methods fall into two categories: image recognition is intuitive and convenient but is affected by morphological differences, environmental factors, and similarity between closely related species; DNA barcoding technology has high accuracy and can distinguish morphologically similar species but requires professional equipment and cannot directly obtain DNA information from images.

The core innovation of DNAVMM is the fusion of DNA and visual data. The model accepts image and DNA sequence inputs, extracts visual features via a visual encoder (e.g., pre-trained CNN or Vision Transformer), processes gene sequences using a DNA sequence encoder (e.g., Transformer or specialized embedding method), and outputs classification results after integrating features through a multimodal fusion module. This fusion strategy can overcome the limitations of single modalities and improve recognition capability and confidence.

This model can be applied in scenarios such as biodiversity survey and monitoring (improving efficiency), citizen science projects (lowering identification thresholds), museum specimen digitization (accelerating resource sharing), and early warning of invasive species (integrating morphological and genetic information), promoting the development of ecological research.

As an open-source project, DNAVMM supports reproduction, improvement, and community collaboration. Usage recommendations include: establishing strict data quality control processes; evaluating the model's generalization ability in new environments and new taxa; combining automatic identification results with the judgments of professional taxonomists to avoid replacing traditional taxonomy.

In the future, DNAVMM can explore more multimodal fusions (sound, geographic distribution, etc.), application of large model technologies (injecting biological domain knowledge), and real-time identification systems (combining edge computing and mobile devices) to further promote breakthroughs in the interdisciplinary field of biological AI.