Surface inspection plays a critical role in the wood industry, as it helps companies to enhance product quality, improves the utilization of wood resources, and increases the value of final products. In recent years, deep learning has emerged as a promising technique in this domain, offering significant advantages over traditional methods by enabling high-precision, real-time inspection. This paper presents a comprehensive review of advancements in the field from 2021 to 2025. It begins with a brief overview of three foundational aspects: common types of wood defects, publicly available datasets, and evaluation metrics. The core of the review then examines recent deep learning applications, organized according to three computer vision tasks—classification, detection, and segmentation. The paper concludes by discussing key challenges and proposing viable directions for future research, thereby offering a clear technical roadmap.

In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to the real-world application stage. Despite the encouraging performance achieved by GNN, less attention has been paid to the privacy-preserving training and inference over distributed graph data in the related literature. Due to the particularity of graph structure, it is challenging to extend the existing private learning frameworks to GNN. Motivated by the idea of split learning, we propose a server aided privacy-preserving GNN (SAPGNN) for the intra-graph node level task on the horizontally partitioned cross-silo scenario. It offers a natural extension of centralized GNN to the isolated graph with max/min pooling aggregation, while guaranteeing that all the private data involved in the computation still stays with local data holders. To further enhance the data privacy, a secure pooling aggregation mechanism is proposed. Theoretical and experimental results show that the proposed model achieves the same accuracy as the one learned over the combined data.

The noise corruption problem commonly exists in hyperspectral images (HSIs) and severely affects the accuracy of hyperspectral unmixing algorithms. The noise formulation existing in HSIs is relatively complex and would change in conjunction with different devices and imaging settings. For real applications, applying denoising approaches without accurate close-to-reality noise modeling before unmixing may not improve, but rather degrade the unmixing performance. This study proposes a robust hyperspectral unmixing method with practical learning-based hyperspectral image denoising. We formulated a close-to-reality noise model for hyperspectral data and provide a calibration approach for the noise parameters. On the basis of the calibrated noise model, synthetic data were generated and used for training a KST-based denoising network. The noisy hyperspectral data were firstly denoised by the trained denoising network and were then used to perform the unmixing process. A variety of unmixing algorithms can be integrated into our method to improve the accuracy of unmixing in noisy situations. In the experiments, several widely used unmixing algorithms were employed to verify the effect of the proposed method. The experimental results on both synthetic and real demonstrated that our proposed method can handle HSI data with various gain settings and helps to improve the unmixing performance effectively.

Knot detection is an important aspect of timber grading. Reducing the false-positive frequency of knot detection will improve the accuracy of the predicted grade, as well as the utilization of the graded timber. In this study, a framework for timber knot detection was proposed. Faster R-CNN, a state-of-the-art defect identification algorithm, was first employed to detect timber knots because of its high true-positive frequency. Then, an overlapping bounding box filter was proposed to lower the false positive frequency achieved by Faster R-CNN, where a single knot is sometimes marked several times. The filter merges the overlapping bounding boxes for one actual knot into one box and ensures that each knot is marked only once. The main advantage of this framework is that it reduces the false positive frequency with a small computational cost and a small impact on the true positive frequency. The experimental results showed that the detection precision improved from 90.9% to 97.5% by filtering the overlapping bounding box. The framework proposed in this study is competitive and has potential applications for detecting timber knots for timber grading.

This paper presents a co-design method for the networked mean square (MS) stabilization problem of multi-input systems with multi-periodic sampling over packet dropout channels, where the control law is sampled with different sampling period and then transmitted via communication channels subject to data loss constraints. In order to solve this problem, a pair of encoder and decoder is introduced for fully utilizing the resource of communication channels. A solvability condition is conducted and reveals the fundamental limitation among the H 2 norm of the system, packet loss rates, and coding matrices. Finally, a numerical example is included to show the effectiveness of the current results.

Knot detection is a challenging problem for the wood industry. Traditional methodologies depend heavily on the features selected manually and therefore were not always accurate due to the variety of knot appearances. This paper proposes an automated framework for addressing the aforementioned problem by using the state-of-the-art YOLO-v5 (the fifth version of You Only Look Once) detector. The features of surface knots were learned and extracted adaptively, and then the knot defects were identified accurately even though the knots vary in terms of color and texture. The proposed method was compared with YOLO-v3 SPP and Faster R-CNN on two datasets. Experimental results demonstrated that YOLO-v5 model achieved the best performance for detecting surface knot defects. F-Score on Dataset 1 was 91.7% and that of Dataset 2 was up to 97.7%. Moreover, YOLO-v5 has clear advantages in terms of training speed and the size of the weight file. These advantages made YOLO-v5 more suitable for the detection of surface knots on sawn timbers and potential for timber grading.

Due to the unique characteristics of decentralization and security, blockchain is believed to have considerable potential to provide a wide range of benefits for ed-ucation development. Its application in education is relatively new but increasing very quickly. This paper introduced the typical blockchain techniques and charac-teristics briefly. Then, recent applications of blockchain in education were sum-marized comprehensively, especially those regarding learning record keeper, cer-tificate issue and management, and decentralized education ecosystem. Finally, technical and non-technical challenges were discussed. It is hoped to provide an in-depth look at the perspectives of blockchain in evolving education and help to the development of new application systems.

In this study, the hyperspectral imaging technology is employed for accurately and efficiently detecting the surface damage of Korla fragrant pears. Eighty fragrant pears were considered in this study. The hyperspectral images of the intact and damaged samples in the wavelength range of 400-1000 nm were obtained. The hyperspectral image obtained at 863 nm was selected to achieve image mask using the statistical analysis method. The dimension of hyperspectral data was reduced via principle component analysis. Subsequently, the second principle component image exhibiting the most considerable difference between the damaged and background areas was selected to compare with the fourth principle component image via the ratio method of image processing for enhancing the difference between the damaged area and the background area. Finally, the threshold segmentation and morphological operations were used to obtain the damaged areas on the surface of fragrant pears. Results denote that the proposed method can effectively identify the surface damage of fragrant pears. Furthermore, the accuracy, precision, and recall rate of the proposed method arc 93.75% , 87.50%, and 100%, respectively.

[目的]研究我国农产品价格与居民消费价格指数(CPI)的动态影响关系,为合理调控农产品价格和促进市场经济稳定发展提供依据.[方法]基于2010年1月—2019年5月的CPI及粳稻、玉米和大豆3种农产品市场价格指数,构建SVAR模型,采用协整检验、Granger因果检验、脉冲响应函数、方差分解方法,探究农产品价格指数与CPI的内在关系.[结果]描述性统计结果表明,农产品价格方差由大到小依次是玉米(9.274)、粳稻(7.328)、大豆(4.252),说明玉米价格波动最大,其次是粳稻价格,大豆价格较稳定.相关性分析结果表明,CPI分别受粳稻价格指数、玉米价格指数和大豆价格指数显著性正影响(P<0.01),相关系数分别为0.747、0.546和0.681.粳稻、玉米和大豆的价格指数与CPI均存在Granger因果关系和长期协整关系.粳稻、玉米和大豆的价格指数对CPI的长期影响系数分别为-0.192、0.069和-0.125,调整速度分别为-0.202、0.003和-0.258,短期影响系数分别为-0.100、0.004和-0.010,表明农产品价格与CPI偏离长期均衡状态时,通过误差修正作用进行有效调整.由脉冲响应函数结果可知,粳稻价格指数和大豆价格指数受外部冲击给CPI短期内带来正影响,玉米价格指数受外部冲击给CPI短期内带来负影响;方差分解结果表明,玉米价格指数对CPI波动产生正向的长期均衡作用,而粳稻价格指数和大豆价格指数对CPI产生负的长期均衡影响,但效果并不显著.[建议]生产者应以建设农业现代化为导向,对农产品进行系统的科学管制,打造产业化、规模化、数字化农业;政府应参与宏观调控农业贸易中的价格波动,制定战略性生产规划,提高定价水平、市场资源配置和整合能力.

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image superresolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.

立木胸径是森林资源调查的重要指标.为了快速、精准地获取立木胸径,基于容栅传感器设计并实现了一款测量装置,实现胸径测量、数据上传、存储分析一体化.装置轻便、手感舒适,理论上最小分辨率可达0.01 mm、满量程测量精度为0.00144％,野外测量试验表明:装置的测量精度高,与卡尺的测量结果比较平均绝对误差MAE为0.36 mm(0.26％)、均方根误差RMSE为0.80 mm(0.48％),与围尺的测量结果比较平均绝对误差MAE为4.72 mm(3.10％)、均方根误差RMSE为6.29 mm(3.07％);装置的测量效率高,平均每棵树木耗时仅需10.64 s;相比传统卡尺,效率提高5倍以上;相比传统围尺,效率提高6倍以上.

针对锯材的节子缺陷,基于空气耦合式超声波的传输系数与待测样本密度之间的关系,探索了空气耦合式超声波的节子缺陷检测新方法,并采用自行研制的空气耦合式超声波检测仪对杉木锯材试样进行检测,结果表明,空气耦合式超声波技术不仅能检测出锯材表面可见的节子缺陷,也可以有效检测不可见的内部节子缺陷.

Methods suitable for the determination and classification of green timber mix (western hemlock and amabilis fir), with respect to species and moisture content, were developed and tested using near infrared spectroscopy and chemometrics. One thousand two hundred samples were distributed into a calibration set (720 samples) and a prediction set (480 samples). Partial least squares (PLS) and least squares-support vector machines (LS-SVM) for both regression (PLSR and LS-SVR) and classification (PLS-DA and LS-SVC) with different spectral preprocessing methods were implemented. LS-SVM outperformed PLS models for both regression and classification. The coefficient of determination (R2p) and root mean square error (RMSEP) of prediction for the best LS-SVR model with spectra pretreated by smooth and first derivative were 0.9824 and 8.7%, respectively, for wood moisture content prediction in the range of 30% to 253%. The best classification model was LS-SVC with spectra pretreated by smooth and second derivative, with overall accuracies of 99.8% in the prediction set, when the samples were divided into four classes. NIRS combined with LS-SVM can be used as a rapid alternative method for qualitative and quantitative analysis of green hem-fir mix before kiln drying. The results could be helpful for sorting green hem-fir mixes with an on-line application.

Wavelength selection is a challenging job for the detection of the bruises on pears using hyperspectral imaging. Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. A novel framework was proposed in this work to increase the performance of the bruise detection, through combining three state-of-the-art variable selection methods and the concept of feature-level integration. Successive projection algorithm, competitive adaptive reweighted sampling, and RELIEF were first applied to the spectra of the Korla pear, respectively. Then, the corresponding feature wavelength subsets were integrated and an optimal feature wavelength set was constructed. An ELM-based classifier was employed for the pear bruise identification finally. Experimental results demonstrated that the feature wavelength integration resulted in lower detection errors. The proposed method is simple and promising for bruise detection of Korla pears, and it can be utilized for other types of defects on fruits.

总磷(TP)、悬浮物浓度(SS)、浊度(TUB)3种水质参数可以直接通过遥感反演得到,常用于评价区域水环境的污染状况.以浙江农林大学东湖为研究对像,使用无人机携带多光谱传感器(Mica Sense Red Edge)获取多光谱影像,进而提取16个光谱参数,分别构建东湖水域TP、SS、TUB的反演模型.结果表明:光谱参数V5(NIR 0.770～0.890 μm)与TP、SS相关性显著(r分别为0.470、-0.537,p＜0.05),V4(0.670～0.760μm)与TUB相关性显著(r=0.486,p＜0.05).在建立的rP反演模型中,指数函数模型精度最高,决定系数R2为0.7829;在建立的SS、TUB反演模型中,多项式函数模型精度最高,决定系数R2分别为0.7503、0.7334.经检验,TP、SS、TUB模型估测值与实测值线性拟合曲线的决定系数R2分别为0.7374、0.8978、0.6726,满足水质要素反演的精度要求.最后利用建立的模型,结合多光谱影像数据,建立了东湖水域各参数的空间分布图,实现了水质参数的可视化,可为小微水域的污染防治提供技术支撑.

为了获得木材径切面上的缺陷形状、 大小和位置,提出一种木材径切面内部缺陷成像的方法.首先,基于应力波在木材径切面上的传播规律,提出一种木材径切面上的应力波速度修正方法.将应力波速度转换为径切面上的若干个预估点的值,结合反距离加权插值(IDW)法提出一种速度修正插值(VCI)方法.最后,使用VCI方法在不同树木样本上进行了木材径切面缺陷的二维成像实验.结果表明:①VCI方法可以重建木材内部缺陷大小以及缺陷位置,缺陷成像结果与真实的缺陷情况相吻合.②对比IDW方法的成像结果,VCI方法对缺陷的大小以及缺陷形状、 位置的成像结果有较大提高.③利用混淆矩阵方法对VCI与IDW方法进行定量分析表明,VCI方法的平均准确率、 平均精确度和平均查全率均高于IDW方法,说明VCI方法成像效果的可行性和有效性.

利用声波的传播速度构建声学断层图像,已成为一种有效的木材无损检测方法.在实践中发现,树干横截面形状对林木声学断层成像的精度有显著影响.然而,现有研究都是假设树干为圆形的,导致生成的声学断层图像与实际情况不符.为了精确检测原木、活立木的树干横截面形状,研制了一种基于激光测距传感器的树干横截面轮廓检测仪器.采用滚珠丝杆在树干周围建立等边三角形,并利用步进电机驱动激光测距传感器沿滚珠丝杆移动,检测不同位置上树干表面的样本点与滚珠丝杆间的垂直距离,然后通过坐标平移、坐标旋转等坐标转换方法将样本点的测量数据转换到同一个坐标系,最后利用3次样条插值算法对所有样本点进行曲线拟合,可以方便地绘制出待测树干的轮廓.详细介绍了检测仪器的机械系统、电路系统、软件系统的设计方法,并分别采用近似圆形和不规则形状的原木样本进行了实验.实验结果表明,该仪器绘制的树干轮廓与真实情况基本一致,生成的轮廓曲线长度与树干周长之间的相对误差为0.83％.

In order to detect the size, shape and degree of decay inside wood, a three-dimensional stress wave imaging method based on TKriging is proposed. The method uses sensors to obtain the stress wave velocity data sets by hanging around the timber randomly, and reconstructs the image of internal defect with those data sets. TKriging optimizes structural relationship between interpolation point and reference point in space firstly. The searching radius is used to select the reference points accordingly. Top-k query method is introduced to find the k value with relevant points. The values of the estimated points are calculated and three-dimensional image of the internal defect inside wood is reconstructed. The results show the effectiveness of the method and the accuracy rate is higher than that of basic Kriging method.

Surface defect detection plays an important role in the selection and utilization of wood. A method is proposed for knot defect detection and localization based on the feature of gray and texture on the wood surface. First, the image is divided into blocks with equal sizes. The gray histogram of each subimage is calculated, and the gray maximum entropy is used as the criterion to achieve the preliminary recognition of the subimage. Second, the texture features of the preliminary result are extracted by local binary patterns algorithm. The support vector machine classification algorithm is utilized to precisely recognize the knot images. Finally, the subimages judged as knot images are joined together to obtain the final result. The experimental results show that the proposed method can obtain commendable recognition results. The knot recognition accuracy reaches 95％ when confusion matrix is used as the evaluation criterion.