Wood Quality Classification Based on Texture and Fiber Pattern Recognition using HOG Feature and SVM Classifier Wood as a material for household appliance needs to be considered of quality. Quality of wood can be classified according to colours, texture, and wood fibre pattern differences. In general, wood industries have been doing the wood quality classified process using a conventional method with a sense of vision in which the results are subjective in terms of accuracy and time efficiency. Machine Learning is a solution to this problem of predicting and classifying data of wood quality. In this paper, the wood will be recognized using Histogram of Oriented Gradient to know the pattern and texture. Meanwhile, the classification method uses Support Vector Machine which will be compared to find the best accuracy and time computation. This system is given image input with five types of cedar classification such as Class A, Class B, Class C, Class D, and Class E took using Logitech C930e HD which is integrated with Arduino Uno for object detection process and conveyor. The Experiment achieve 90% of accuracy with time computation 1,40 s

Locating Wood Pith in a Wood Stem Cross Sectional Image Using YOLO Object Detection Sawmills rely on locating wood pith at the end surface of a wood stem either to automate the process and maximize the yield or to perform wood quality assessment of wood planks. Previously, wood pith location was approximated by applying image processing algorithms along with some geometric relations between the annual rings within a crosssectional image of wood and its pith. This produces failed detection results if the wood stems deviate from a circular shape. In this paper, we propose employing YOLO (you only look once) object detection to a wood pith identification problem. The TinyYOLO network with 23 layers was trained using a transfer learning approach with 345 wood crosssectional images with pith annotations. Different experiments with variable ratios between the training and testing datasets and training iterations were carried out to produce checkpoints and test against the testing datasets. We found that the minimum loss function which is commonly used to indicate the best trained model in other deep neural networks (DNNs) object detection is not effective for this application. Therefore, we used detection accuracy, to reflect the percentage of detectable piths while inferencing the trained model with its testing dataset and an average distance error, i.e., the average distances between the detected piths and their ground truths, as the accuracy measurement. The experimental result shows that a ratio of training and testing datasets of 70:30, at training iterations of 170,000, gives rise to the maximum detection value of 76.3% with a 16.6pixel average distance error. Additionally, it produces only 25% average distance error; frac10344176 with 50% standard deviation; frac25335395, compared to the state of the art nonDNNbased pith detection approach.