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In several processes of the forest products industry, an in-depth knowledge of log and board internal features is required and their determination needs fast scanning systems. One of the possible technologies is X-ray computed tomography (CT) technology. Our paper reviews applications of this technology in wood density measurements, in wood moisture content monitoring, and in locating internal log features that include pith, sapwood, heartwood, knots, and other defects. Annual growth ring measurements are more problematic to be detected on CT images because of the low spatial resolution of the images used. For log feature identification, our review shows that the feed-forward back-propagation artificial neural network is the most efficient CT image processing method. There are also some studies attempting to reconstruct three-dimensional log or board images from two-dimensional CT images. Several industrial prototypes have been developed because medical CT scanners were shown to be inappropriate for the wood industry. Because of the high cost of X-ray CT scanner equipment, other types of inexpensive sensors should also be investigated, such as electric resistivity tomography and microwaves. It also appears that the best approach uses various different sensors, each of them having its own strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Log moisture content (MC) has been determined based on the propagation velocity (PV) of ground penetrating radar (GPR) signals. This approach is based on measuring the travel time of the GPR signal through the log, from which its PV and the apparent log dielectric permittivity can be retrieved. Linear regression between the log dielectric permittivity and MC was established for each of the investigated wood species (quaking aspen, balsam poplar, and black spruce), log state (thawed and frozen), and direction of measurement [on the log cross-section (CS) and through the bark (TB)]. CS and TB measurements led to different results depending on the log state and wood species. Linear models with different slopes were found for thawed (slope = 6.4-9.8) and frozen (slope = 12- 29) logs due to the difference in the dielectric properties of the frozen and unfrozen water in wood. The models for quaking aspen and balsam poplar were very similar to each other and differed from that of black spruce in terms of slopes and intercepts. Generally, the PV method leads to poorer log MC prediction accuracy than the partial least squares method presented in Part 1 of this study. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a handheld system showing good potential for the real-time and nondestructive characterization of wood moisture content (MC). However, measurements performed over logs can be challenging because of their curved surface that can affect the GPR signal. In this study, the MC of thawed and frozen logs was estimated for three species (quaking aspen, balsam poplar, and black spruce) using the full GPR signals and the partial least squares (PLS) regression method. The signal was acquired from the cross-section (CS) and through the bark (TB) of the logs with and without an aluminum plate placed under the log. The full GPR signal does not provide better log MC prediction accuracy for small logs compared with the early-time GPR signal. The information about the shape and diameter of the log is contained in the direct and reflected waves of the GPR signal. CS models provided more accurate log MC prediction (RMSEv = 7-25%) than TB models (RMSEv = 6-40%) for the hardwood species. Thawed and frozen log models showed similar performances. This study demonstrates that GPR in combination with PLS regression is suitable for predicting log MC in the field. [ABSTRACT FROM AUTHOR]