Perspectives on deep learning for near-infrared spectral data modelling

Passos, Dário; Mishra, Puneet

doi:10.1177/09603360221142821

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Convolutional neural networks (CNNs), which are excellent algorithms used in DL approaches, are renowned for their capability to enhance performance in image (two-dimensional (2D) data) classification [17]. This method has also been introduced into NIR spectroscopy modeling [18]; to match NIR spectral data (one-dimensional (1D) data), a 1D convolutional kernel is used in the model, and the result is called the 1D CNN model. In the investigations of Yang et al [1] and Pan et al [19], the integration of 1D CNN with NIR spectra resulted in the more successful identification of wood species as compared to conventional NIR spectral modeling methods.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

Pan,

Yu,

Yang

2024

Forests

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The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

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Section: Introductionmentioning

confidence: 99%

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

Pan,

Yu,

Yang

2024

Forests

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

Pan,

Yu,

Yang

2023

Holzforschung

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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.

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Perspectives on deep learning for near-infrared spectral data modelling

Cited by 2 publications

References 4 publications

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

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

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