As an important renewable resource, wood is widely used in various industries. When addressing wood defects that limit the amount of wood used during processing, manual inspection and other technologies are not suitable for automated production scenarios. In this paper, we first establish our own dataset, which includes information about multiple tree species and multiple defects types, to enhance the overall applicability of the proposed model. Second, target detection technology involving deep learning is used for defect detection. The conditional parametric convolution (CondConv), Wise-IoU, and BiFormer modules are used to improve upon the latest YOLOv8 algorithm. Based on the experimental findings, the suggested approach exhibits notable improvements in terms of both the mAP@0.5 index and the mAP@0.5:0.95 index, surpassing the performance of the YOLOv8 algorithm by 3.5% and 5.8%, respectively. It also has advantages over other target detection algorithms. The proposed method can effectively improve wood utilization and automated wood processing technology.

Aiming at the model overfitting problem caused by limited datasets and visual complexity in succulent plant classification tasks, this study proposes a GoogLeNet classification method based on lightweighting and improving the Convolutional Block attention module (CBAM). Meanwhile, batch normalization (BN) operations are added after each convolutional layer to accelerate network convergence and improve model stability. In addition, the model’s ability to extract key features is enhanced by integrating the channel and spatial attention mechanisms of the CBAM attention module. Experimental results show that the improved lightweight GoogLeNet achieves 99.4% classification accuracy on the validation set, effectively mitigates the overfitting problem, and maintains high computational efficiency in resource-constrained environments. The model parameters and computational complexity are significantly reduced by streamlining the Inception modules from nine to seven and introducing depth-separable convolution. To further validate the model robustness, this study extends the dataset via data augmentation methods, and the experiments show that the improved model still maintains stable performance in small dataset environments, demonstrating its advantages in data-scarce scenarios. This study provides an effective solution for the task of succulent plant classification, which has significant application value. Future research will focus on further optimization of the model structure to continuously improve the classification accuracy and robustness.

In wood defect detection, factors such as few-shot sample scarcity, diverse defect types, and complex background interference severely limit the model’s recognition accuracy and generalization ability. To address the above issues, this paper proposes an improved Faster RCNN model based on a dual attention mechanism (DAM). The model integrates cross-attention and spatial attention modules to enhance the expression of key region features, suppresses texture noise interference; the improved Wood-Region Proposal Network (WRPs) module utilizes feature mean pooling and cross-layer fusion strategies to significantly improve the quality and robustness of candidate box generation; in addition, the Wood-Feature Reconstruction Head (WFRH) module effectively enhances the adaptability to new classes and few-shot defects through multi-branch classification and weighted fusion mechanisms. After synergistic optimization of all modules, the model demonstrates superior detection accuracy and category discrimination capability. Experimental results show that the proposed method achieves state-of-the-art performance on the PASCAL VOC and FSOD datasets, particularly in the identification of 17 types of wood defects, where AP50 and AP75 are improved by 25% and 7.9%, respectively, validating the significant advantages of the proposed DAM mechanism under few-shot and complex background conditions. The findings of this study provide practical technical references for intelligent and efficient few-shot detection in real-world wood quality inspection tasks.

Accurate and efficient wood defect detection is crucial for improving timber utilization rates in the wood processing industry. To address the limitations of existing detection models in terms of accuracy, efficiency, and generalization, this study proposes EFCW-YOLO, an improved model based on YOLOv8n. The model incorporates four key enhancements: the Focal Modulation module for superior context modeling, the Efficient Channel Attention (ECA) mechanism for lightweight channel attention, the C2fCIB module for efficient feature extraction, and the Wise-IoU v3 loss function for robust bounding box regression. Experimental results on the self-built PlankDef-9K multi-species defect dataset demonstrate that EFCW-YOLO achieves a mean average precision (mAP50) of 90.26%, outperforming several mainstream detectors while maintaining a compact model size of 6.12 MB. Furthermore, the model exhibits strong generalization capability, as validated on a cross-domain crop pest dataset. The study concludes that EFCW-YOLO provides a high-performance, lightweight solution for automated wood surface defect detection, offering significant potential for industrial deployment to reduce waste and enhance processing efficiency. This technology holds significant practical potential for enabling precise utilization of wood resources, reducing raw material waste, and enhancing the economic benefits of the wood processing industry.

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage.

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average.