Bark Removal

Bark removal and analysis are undergoing a technological transformation driven by advancements in computer vision and machine learning. Current research focuses on automating tasks like wood plate segmentation in industrial settings and tree species identification using bark texture analysis, employing convolutional neural networks (CNNs) and transfer learning techniques to achieve high accuracy. These advancements are improving efficiency in forestry and wood processing, while also contributing to improved species identification and conservation efforts. The development of large, publicly available datasets is crucial for further progress in this field.