Abstract: A black and white image of a wood product, such as a veneer sheet, is captured with a first camera and a color image of the wood product is captured with a second camera. Computer processing of the black and white image is performed to determine dimensions of the wood product, the existence of voids within the wood product, and the presence of debris on the wood product. Computer processing of the color image is performed to determine whether colored defects are present in the wood product. A grade is assigned to the wood product based on this computer processing. The wood product can then be sorted based on the grade.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based surface irregularity prediction models, are used to accurately identify surface irregularities on a surface of a wood product, such as a veneer sheet or ribbon, and provide irregularity prediction data for the wood product. Based on the irregularity prediction data for a given wood product, one or more actions are taken with respect to wood product or the production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and one or more visual cameras are used to generate superimposed image data representing a visual/NIR composite image of wood product and correlating moisture levels with physical features of the surfaces of the wood product. By analyzing the visual/NIR composite image represented by superimposed image data, moisture pockets near an open physical feature that, absent the presence open physical feature would be a problem, can be identified and ignored. Based on the identified moisture levels at various locations in a given wood product, and the proximity of physical features to the identified moisture locations, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, are used to detect irregularity in the surface of a wood product. Based on the detected irregularities at various locations in a given wood product, one or more actions are taken with respect to a production process used to produce the wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify surface irregularities on a surface of a veneer sheet or other wood product. Based on the identified surface irregularities for a given wood product, one or more actions are taken with respect to wood product or production process to ensure the wood product is put to the most efficient, effective, and valuable use.

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, is used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product. Based on the identified moisture content for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put the most efficient, effective, and valuable use.

Abstract: Exemplary fire resistant construction members disclosed herein can comprise an engineered wood product I-joist and at least two panels comprising gypsum board secured to the I-joist. The member can comprise a first gypsum board panel secured to a first side of the member and a second gypsum board panel secured to a second, opposite side of the member. In some embodiments, gypsum board panels are secured to opposite sides of a web of the I-joist. In some embodiments, gypsum board panels are secured to opposite sides of upper and lower flanges of the I-joist.