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Accurate and rapid wood species identification is vital for wood utilization and trade. This goal is achievable with the fast development of deep learning (DL). Several studies have been published related to this topic; however, they were limited by their generalization performance in practical applications. Therefore, this study proposed a DL multimodal fusion framework to bridge this gap. The study utilized a state-of-the-art convolutional neural network (CNN) to simultaneously extract both short-wavelength near-infrared (NIR) spectra and RGB image feature, fully leveraging the advantages of both data types. Using portable devices for collecting spectra and image data enhances the feasibility of onsite rapid identification. In particular, a two-branch CNN framework was developed to extract spectra and image features. For NIR spectra feature extraction, 1 dimensional NIR (1D NIR) spectra were innovatively encoded as 2 dimensional (2D) images using the Gramian angular difference field (GADF) method. This representation enhances better data alignment with CNN operations, facilitating more robust discriminative feature extraction. Moreover, wood’s spectral and image features were fused at the full connection layer for species identification. In the experimental phase conducted on 16 difficult-to-distinguish wood samples from the Lauraceae family, all achieved identification metrics results exceed 99 %. The findings illustrate that the proposed multimodal fusion framework effectively extracts and fully integrates the wood’s features, thereby, improving wood species identification.

A deep learning multimodal fusion framework for wood species identification using near-infrared spectroscopy GADF and RGB image