The research discusses a new methodology for lumber identification based on blockchain technology, deep learning, and perceptual hashing algorithms. The development aims to provide traceability of wood resources along the entire supply chain (i.e., from logging to final consumption). The uniqueness of the method lies in utilizing the natural features of tree rings that act as “biometric fingerprints” to form identifiers. These identifiers, generated through key feature extraction algorithms (SIFT, ORB, BRISK, and AKAZE), are converted into perceptual hashes, which are subsequently written to an immutable blockchain database. The research covers the process of capturing wood faces in a logging environment (e.g., using cameras on harvesters), which enables the generation of unique digital casts of wood samples already at the production stage. The system successfully utilizes deep neural networks (YOLOv8s) for segmentation and detection of wood ends, which improves the accuracy of key feature extraction at the level of wood ring textures. The generated hashes, which are robust to minor image changes, are written to a blockchain, ensuring data security against tampering. The proposed approach has a high practical significance for the forestry industry because it contributes to the effective fight against illegal logging, improving the processes of accounting and management of wood resources. The research was conducted using the example of regional enterprises, which confirms the feasibility of the method in real production conditions.