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This review article examines past and current research on the application of near-infrared (NIR) reflectance/transmittance spectroscopy (NIRS) for real-time monitoring of moisture content and density of solid wood. Most of the applications of NIRS on solid wood have focussed on the application of multivariate statistics as exploratory tools for the prediction of physical, chemical and mechanical properties, such as moisture content, density, stiffness, cellulose and lignin content. However, very few studies on the development of optical models and the use of NIRS transmittance techniques on solid wood have been reported. NIRS technology has the potential to be used as a rapid tool that could be employed for at-line measurement and monitoring of wood properties in the forest products industry.Keywords: wood properties, near-infrared spectroscopy, multivariate statistics, optical model, transmittance, reflectance résumé Cet article de revue examine la recherche passée et actuelle sur l'application de la spectroscopie proche infra-rouge en mode réflectance/transmittance pour le suivi en temps réel de la teneur en eau et de la densité du bois solide. La plupart des applications de la spectroscopie proche infra-rouge sur le bois solide ont porté sur l'application des méthodes statistiques multi-variées pour l' estimation des propriétés physiques, chimiques et mécaniques, telles que la teneur en eau, la densité, la dureté, le contenu en cellulose et en lignine. Cependant, peu d' études ont porté sur le développement de modèles optiques et sur l'utilisation de la spectroscopie proche infra-rouge en mode transmittance pour le bois solide. La spectroscopie proche infra-rouge a le potentiel d' être utilisée comme un outil rapide de mesure et suivi en temps réel des propriétés du bois dans l'industrie des produits forestiers.

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Potato late blight, caused by Phytophthora infestans, is a major disease worldwide that has a significant economic impact on potato crops, and remote sensing might help to detect the disease in early stages. This study aims to determine changes induced by potato late blight in two parameters of the red and red-edge spectral regions: the red-well point (RWP) and the red-edge point (REP) as a function of the number of days post-inoculation (DPI) at the leaf and canopy levels. The RWP or REP variations were modelled using linear or exponential regression models as a function of the DPI. A Support Vector Machine (SVM) algorithm was used to classify healthy and infected leaves or plants using either the RWP or REP wavelength as well as the reflectances at 668, 705, 717 and 740 nm. Higher variations in the RWP and REP wavelengths were observed for the infected leaves compared to healthy leaves. The linear and exponential models resulted in higher adjusted R2 for the infected case than for the healthy case. The SVM classifier applied to the reflectance of the red and red-edge bands of the Micasense® Dual-X camera was able to sort healthy and infected cases with both the leaf and canopy measurements, reaching an overall classification accuracy of 89.33% at 3 DPI when symptoms were visible for the first time with the leaf measurements and of 89.06% at 5 DPI, i.e., two days after the symptoms became apparent, with the canopy measurements. The study shows that RWP and REP at leaf and canopy levels allow detecting potato late blight, but these parameters are less efficient to sort healthy and infected leaves or plants than the reflectance at 668, 705, 717 and 740 nm. Future research should consider larger samples, other cultivars and the test of unmanned aerial vehicle (UAV) imagery for field-based detection.