Possibilities of image analysis for quality wood sorting

Wood assessment optimization should be the top priority of the forestry subjects that are fundamentally dependent on the income from its sale. The aim of this paper is to analyse the beech, oak and ash tree logs that were categorized into quality classes according to the size of one of the qualitative characters related to the surface area (false heartwood, rot). The classical methodology used in forestry was compared with the application of ImageJ software. In total, thirty logs were analysed. The characters of false heartwood and rot were chosen and evaluated according to their size on the log end. There were no other characters that obstructed the categorization into quality classes. The ImageJ software application led to improved assessment (transfer to a higher quality class) in 56% of the logs. The volume of the evaluated assortments was 18.43 m3. The total difference in the value of the assortments with the ImageJ software application reached + €70.44 (+ 4.7%). The analysis therefore confirmed that in case of a considerable irregularity in a qualitative character (when the surface area of the character significantly differs from the circumscribed circular surface), the standard STN EN 1309–3 methodology systematically overvalues the surface area of this character. That affects the assessment potential of the specific log

Innovative methods of non-destructive evaluation of log quality

For the sustainability of an important renewable resource, such as wood, it is important to significantly increase the efficiency of its processing. A large part of this raw material ends up in the wood processing industry, where it is used for the production of pulp, paper, construction and furniture timber, floors and others. Therefore, it is very important to gain the knowledge needed for optimal valuation of raw wood material, through quality detection and classification into quality classes. There are many defectoscopic methods working on different physical principles. The most familiar of these methods are semi-destructive and non-destructive, as they do not cause damage to the tree or wood during assessment. The aim of this article is to describe, assess and compare known semi-destructive and non-destructive methods for the assessment of wood properties. This article describes basic visual inspection, basic semi-destructive methods (Pilodyn, Resistograph) and advanced semi-destructive methods (SilviScan®, DiscBot®) as well. Non-destructive methods use mostly acoustic wave motion (acoustic, ultrasonic), high-frequency waves (using georadar, microwave) and methods based on visual evaluation (image, laser). At last, there are X–ray methods with the latest technology using three-dimensional (3D) computed tomography (CT). The implementation of modern non-destructive methods is of great importance for the application of principles of Industry 4.0, where these methods provide collecting of data on the material properties, in its entire production flow of log processing

Neural Networks - A Way to Increase the Fuel Efficiency of Vehicles

This paper deals with the possibility of creating a vehicle model using a hierarchy of neural networks. Based on this model, it is possible to build an optimization cycle that looks for parameters which are influencing the driving of vehicles along given path. The given path must include a driving through the town, out of town and along the highway section, so the test track contains the greatest number of driving modes. Data for neural network are obtained from the CAN bus and the GPS sensor. Based on the built model and given route it is looking for such route drive, where it eventually came that the development of fuel consumption is lower than in unoptimized drive

Validation and Application of European Beech Phenological Metrics Derived from MODIS Data along an Altitudinal Gradient

Monitoring plant phenology is one of the means of detecting the response of vegetation to changing environmental conditions. One approach for the study of vegetation phenology from local to global scales is to apply satellite-based indices. We investigated the potential of phenological metrics from moderate resolution remotely sensed data to monitor the altitudinal variations in phenological phases of European beech (Fagus sylvatica L.). Phenological metrics were derived from the NDVI annual trajectories fitted with double sigmoid logistic function. Validation of the satellite-derived phenological metrics was necessary, thus the multiple-year ground observations of phenological phases from twelve beech stands along the altitudinal gradient were employed. In five stands, the validation process was supported with annual (in 2011) phenological observations of the undergrowth and understory vegetation, measurements of the leaf area index (LAI), and with laboratory spectral analyses of forest components reflecting the red and near-infrared radiation. Non-significant differences between the satellite-derived phenological metrics and the in situ observed phenological phases of the beginning of leaf onset (LO_10); end of leaf onset (LO_100); and 80% leaf coloring (LC_80) were detected. Next, the altitude dependent variations of the phenological metrics were investigated in all beech-dominated pixels over the area between latitudes 47°44′ N and 49°37′ N, and longitudes 16°50′ E and 22°34′ E (Slovakia, Central Europe). In all cases, this large-scale regression revealed non-linear relationships. Since spring phenological metrics showed strong dependence on altitude, only a weak relationship was detected between autumn phenological metric and altitude. The effect of altitude was evaluated through differences in local climatic conditions, especially temperature and precipitation. We used normal values from the last 30 years to evaluate the altitude-conditioned differences in the growing season length in 12 study stands. The approach presented in this paper contributes to a more explicit understanding of satellite data-based beech phenology along the altitudinal gradient, and will be useful for determining the optimal distribution range of European beech under changing climate conditions