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Background
The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient tr...
Citations
... Previous studies trained microscopic images of the three sections and then identified the test images [1,34]. Here, we explored the impact of different multiples on identification results and feature visualization. ...
Background
Traditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species.
Result
A total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species.
Conclusion
The research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.
... Additionally, we will introduce classification models, such as multi-view CNN [49], which treat multiple views of an object as the same object for classification to avoid the problem of treating different projections of the same object as separate entities. For example, Silva et al. [50] achieved 95% accuracy in tree species classification by using microscopic images of three major anatomical parts of wood and combining them with the multi-view random forest model, which is different from the traditional approach of using cross-sectional images alone. In future research, we will explore the optimal combination of multi-view images and the multi-view classification model, along with the point cloud data that are currently in use, to further investigate the upper limit of the classification of tree species using multi-view projection images, which can be obtained quickly and conveniently. ...
To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and utilising various classification models on the classification of tree point cloud projected images was investigated. Nine tree species in Sanjiangkou Ecological Park, Fuzhou City, were selected as samples. In the single-direction projection classification, the X-direction projection exhibited the highest average accuracy of 80.56%. In the dual-direction projection classification, the XY-direction projection exhibited the highest accuracy of 84.76%, which increased to 87.14% after adding depth information. Four classification models (convolutional neural network, CNN; visual geometry group, VGG; ResNet; and densely connected convolutional networks, DenseNet) were used to classify the datasets, with average accuracies of 73.53%, 85.83%, 87%, and 86.79%, respectively. Utilising datasets with depth and multidirectional information can enhance the accuracy and robustness of image classification. Among the models, the CNN served as a baseline model, VGG accuracy was 12.3% higher than that of CNN, DenseNet had a smaller gap between the average accuracy and the optimal result, and ResNet performed the best in classification tasks.
... The proposed hardware in collecting and identifying woods based on anatomy images or the Xylo Tron platform is presented in ref [20] as an aid in anatomical wood. Some studies on wood species identification using different algorithms such as the multi-view random forest model (MVRF) [21], Mask-R CNN [22], and ROXAS-an automatic tool based on traditional image processing methods [23]. Convolutional Neural Networks (CNN) is a specific type of artificial neural network that uses perceptrons, it is widely applied to studies on image processing and for many different fields with impressive accuracy [24,25]. ...
... Henriques et al. (2022) updated the elastic modulus parameters for identifying the orthogonal anisotropy of pine by the finite element model. da Silva et al. (2022) introduced a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. ...
Understanding the macro-mechanical behavior of wood at the micro-scale is of great significance for the design of cell-wall-like composite materials and pulp papermaking. In order to predict tracheid mechanical properties and analyze its relationship with tracheid features, based on the FCN network model, a double-channel FCN network with sparse attention (D-SA-FCN) was designed by introducing the double-channel mechanism and the sparse attention mechanism. The features of tracheid of larch were extracted numerically and the data set was established by using the compression strength data, the gray level co-occurrence matrix, cell segmentation and geometric analysis. A feature analysis algorithm based on PCA and random forest was established to optimize the feature values. The training set accuracy of the D-SA-FCN network model reached 85.75% with the five-level mechanical property level according to the classification standard. The accuracy of the training model is 71.48% and 79.52% when the morphological and texture features are input respectively. The results show that texture features had a more significant impact on mechanics to a certain extent and the D-SA-FCN could reduce the computational complexity and improve the prediction accuracy.
HIGHLIGHTS • FT-NIR spectroscopy was capable of discriminating wood from natural fallen trees from the Lecythidaceae family. • Degraded samples carry 50% of the identification bands at the genus level. • The spectral signature is the key to identifying wood types. • The mathematical treatment (2 nd derivate, Norris, and Savitzky-Golay smoothing) increased the identification accuracy. • FT-NIR is a powerful tool for taxonomic purposes in accurate and non-destructive identification and classification of wood. SUMMARY The scientific identification of natural fallen trees in tropical forests is complex due to the lack of fertile material in field collection. The study evaluated the use of near-infrared spectroscopy with Fourier-transform (FT-NIR) in the discrimination of wood from fallen trees of the Lecythidaceae family. Seven trees were collected in the Central Amazonian region (Brazil), from which 63 specimens were prepared from the wood, and NIR spectra were obtained on different wood surfaces (total 756 spectra). Chemometric models were developed with a spectral data set, and the Mahalanobis algorithm was applied. The discriminant model with 2 nd derivative spectra improved the identification capacity, resulting in errors < 5% in the identification of genus Couratari (3 ssp.), Eschweilera (2 ssp.), Holopyxidium (1 sp.) and Lecythis (1 sp.). The comparison of the spectral signatures of samples of fallen trees and wood library revealed that even when wood was exposed to environmental weathering, around 50% of the original bands were preserved, favouring discrimination at the genus level. The accuracy of the chemometric models developed indicates the applicability of FT-NIR spectroscopy integrative in identifying fallen trees from the Lecythidaceae family in the tropical forests.
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross‐scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
Wood identification plays a significant role in forestry and cultural heritage. Its impact and application are more pronounced in the guide against illegal logging and ecological issues. Moreover, it is extremely difficult to identify individual wood species using the existing wood identification methods. Due to the above-mentioned reasons, there is a need to develop an accurate wood identification method that is based on computer vision. Computers react to what they see, just as human eyes react, the insights gained from computer vision as a field of artificial intelligence (AI) enable computers to extract meaningful data from visual inputs such as images and videos, and to take automated actions. Just like AI allows computers to possess thinking ability, computer vision gives them the ability to see. Computer vision, through the region-based convolutional neural network has been able to solve various visual issues that are related to videos and images accurately. However, its application to wood technology and wood related fields still remains uninvestigated. Therefore, in this study, a computer vision based mask region-based convolutional neural network (Mask R-CNN) associated with a modified residual network (ResNet) was employed as a hybrid method (Mask RCNN-ResNet) for a robust and accurate wood identification at species level. The technique of Mask R-CNN involves detecting, extracting the relevant features of visual data from a processed image, then, segmenting the target object using region of interest alignment, and mask generation to make a decision. A modified ResNet network (modified model of residual network) reduced the number of training parameters, thereby leading to an increased efficiency during computation. Mask RCNN-ResNet method is able to identify individual wood at species level with 92% identification accuracy higher than the results obtained in the existing work that used other computer-based wood identification methods on the same wood species datasets.
Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.
Previous reports have shown that wood species identification result based on near-infrared (NIR) spectroscopy was intimately entwined with spectra preprocessing. However, there is no universal recipes for a suitable preprocessing method, and misuse of preprocessing may bring on worse model performance for new species identification. Therefore, a convolutional neural network (CNN) model incorporating a residual connection structure is created aiming at replacing the preprocessing and identifying 21 Pinaceae species at the species level. The model is compared to the other two CNN models on different wavelength range raw transverse section NIR spectra. 12 preprocessing methods are carried out for 780–2440 nm spectra to evaluate the influence of spectra preprocessing on the model. The model outperforms the other two CNN models on raw and preprocessed spectra and provides the highest macro F1 of 0.9787 and 0.9792 for raw and preprocessed spectra at the wavelength range of 780–2440 nm. The model is further compared to three conventional methods. The results indicated that created model is capable to replace the spectra preprocessing and identify 21 wood species at the species level. It is indicated that a suitable CNN structure can replace the multifarious data preprocessing in traditional methods. It potentially provides a generic raw NIR spectra discrimination method for wood species identification.
