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Company News About Application of hyperspectral camera to measure moisture content of wood

Application of hyperspectral camera to measure moisture content of wood

2024-12-13
Latest company news about Application of hyperspectral camera to measure moisture content of wood

Wood moisture content is an important attribute of wood quality, which has an important impact on wood processing, use and storage. Although the traditional methods of measuring wood moisture content such as weighing method and resistance method have certain accuracy, they have some disadvantages such as cumbersome operation, long measurement time and damage to wood. Hyperspectral imaging provides a fast, non-destructive and efficient method for measuring wood moisture content.


一、hyperspectral camera test principle
Hyperspectral cameras can obtain spectral information of the wood surface, which includes the reflectivity or transmission of the wood at different wavelengths. Since the moisture content of wood will affect its spectral characteristics, the moisture content can be deduced by analyzing the spectral information of wood. Specifically, the spectral data of wood can be collected by hyperspectral imaging technology, and the prediction model between wood moisture content and spectral information can be established by pre-processing, feature extraction and modeling, so as to realize the rapid test of wood moisture content.


二、Application examples
Instrument: Color spectrum built-in push sweep FS-17 near infrared high spectrometer
Auxiliary equipment: Constant spectral light source - for indoor modeling
Light source: linear halogen light source

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Experimental materials: A number of wood samples with different moisture content are used as experimental materials, and these wood blocks are cyclically dried to obtain different moisture content states.

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Data acquisition: Spectral image acquisition of wood samples was carried out using hyperspectral imaging system. In the acquisition process, it is necessary to ensure that the lighting conditions are stable to avoid the impact of light changes on the spectral information. At the same time, in order to obtain more accurate results, spectral image acquisition can be carried out at multiple locations of the wood sample, and the average value is taken as the final spectral data.

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Data processing: Pre-processing the collected spectral data, such as removing noise, correcting spectrum, etc. Then feature selection algorithm is used to extract the characteristic wavelength related to wood moisture content to simplify the model and improve the prediction accuracy.

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Model building: Based on the extracted characteristic wavelength, the prediction model between wood moisture content and spectral information was established. Common modeling methods include Gaussian process regression (GPR), partial least squares regression (PLSR) and so on. These models can quickly predict the moisture content of wood based on its spectral information.

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Model validation: The established model is validated using an independent validation set to assess its predictive performance and accuracy. Common evaluation indexes include correlation coefficient (R²) and root mean square error (RMSE).

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三、Application advantages
Fast test: The hyperspectral camera can obtain the spectral information of the wood surface in a short time, so as to realize the rapid test of the wood moisture content.


Non-destructive testing: Compared with traditional testing methods, hyperspectral imaging technology does not cause damage to the wood, so it is more suitable for testing valuable wood or wood that needs to be maintained in integrity.


High accuracy: By establishing an accurate prediction model, hyperspectral cameras can achieve high-precision testing of wood moisture content, meeting the stringent quality control requirements of the wood processing industry.


四、Application prospect
With the continuous development and improvement of hyperspectral imaging technology, its application prospects in wood moisture content testing will be more broad. In the future, we can look forward to the emergence of hyperspectral cameras with higher precision, faster speed and easier operation to meet the needs of the wood processing industry for quality control and intelligent production. At the same time, combined with advanced technologies such as machine learning and deep learning, the accuracy and intelligence level of wood moisture content testing can be further improved.


In summary, hyperspectral cameras have significant advantages in testing wood moisture content, providing an efficient, accurate and non-destructive inspection method for the wood processing industry.

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