Wood Species Identification through Fusion of NIR Spectroscopy and Digital Image Features Information Using Convolutional Neural Networks
[Objective]The feasibility of accurately identifying wood species by integrating near infrared spectroscopy(NIR)and digital image feature information,automatically extracted based on convolutional neural networks(CNN),is investigated.[Method]10 wood specimens from the Lauraceae family were used as examples.NIR spectra and digital images from the transverse surfaces of specimens were collected using a handheld NIR spectrometer and portable scanner.The recurrence plot(RP)method was innovatively employed to encode one-dimensional(1D)NIR spectra as two-dimensional(2D)images.This not only enabled the CNN model to extract more robust discriminative features from the handheld NIR spectrometer's shorter-wavelength NIR spectra,but also facilitated the fusion of NIR spectra and images.A simple two branch CNN(TB-CNN)model was developed to automatically extract and fuse NIR spectral and digital image features for wood species identification.[Result]Compared to different modeling methods that utilize 1D NIR spectra directly,integrating 2D RP of NIR spectra with CNN resulted in a performance improvement ranging from 1.79％to 14％.Furthermore,when compared with the results obtained using a single feature from either NIR spectra or digital image,the TB-CNN method showed a significant increase in identification rates of at least 3％.Notably,the accuracy,precision and recall values all surpassed 99％.[Conclusion]The conversion of 1D short-wavelength NIR spectra into RP enhances the ability of CNN model to extract more discriminative features from the NIR spectral data,improving model performance in species identification.The TB-CNN model effectively extracts and integrates wood NIR spectra and digital image features,addressing the limitations of using a single feature type for wood species identification and improving overall identification accuracy.
wood species identificationconvolutional neural network(CNN)near-infrared spectroscopyimagefeature extraction and fusion
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