An analysis of timber sections and deep learning for wood species classification

Geus, André R. de; Silva, Sérgio F. da; Gontijo, Alexandre Bahia; Silva, Flávio O.; Batista, Marcos Aurélio; Souza, Jefferson R.

doi:10.1007/s11042-020-09212-x

Cited by 18 publications

(18 citation statements)

References 27 publications

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“…For this reason, most studies have used Xylarium collections [26,[47][48][49][50][51][52]. Most studies only captured cross-sectional images of wood blocks except for a few studies using lumber surface [53] or three orthogonal sections [54,55]. The surfaces of the blocks are cut with a knife or sanded with sandpapers to clearly reveal the anatomical characteristics.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…These characteristics make it suitable for wood identification. ANN has been used with large datasets [72,97,128], which supports DL methods such as CNN that are able to build more sophisticated models with larger amounts of data [53,55,64,96].…”

Section: Classificationmentioning

confidence: 99%

“…Tang et al [49] developed a smartphone-based portable macroscopic wood identification system based on the SqueezeNet [164] model. In a comparative study of DL and conventional ML models [55], CNN-based models, Inception-v3 [165], SqueezeNet [164], ResNet [25], and DenseNet [166], all models achieved better performance than k-NN models trained with LBP or LPQ features. Lens et al [72] also reported that VGG16 and ResNet101 models had better classification performance for the UFPR dataset than those trained with texture features.…”

Section: Cnn In Wood Identificationmentioning

confidence: 99%

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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

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The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

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Section: Image Acquisitionmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Cnn In Wood Identificationmentioning

confidence: 99%

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Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Hwang

Sugiyama

2021

Plant Methods

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“…Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In the contribution by Geus et al "An analysis of timber sections and deep learning for wood species classification", authors introduced the biggest data set of wood timber microscope images to date, with 281 species, having three types of timber sections: transverse, radial, and tangential [4]. They investigated the use of transfer learning from pre-trained deep neural networks for wood species classification and compared their results with a state-of-the-art pre-designed feature method.…”

Section: Applicationsmentioning

confidence: 99%

Artificial intelligence in deep learning algorithms for multimedia analysis

Jeon

Anisetti

Damiani

et al. 2020

Multimed Tools Appl

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Deep learning has gained a lot of research interest in artificial intelligence (AI) in many applications, such as image understanding, object detection, feature extraction, audio/ video processing, image demosaicking and denoising, overhead views in industrial applications. In addition, exploitation of deep learning in the field of data science, particularly in big data analytics focuses on high-level feature extraction and abstraction as data representation based on the hierarchical learning process. Moreover, deep learning is also designed to tackle supervised learning problems for a wide variety of tasks. How to reliably solve unsupervised tasks with a similar degree of success is an important issue to address. Such studies are investigated based on the adoption of parallel computing, i.e., GPUs and CPUs clusters.Various algorithms are already applied to achieve the desired goals. For instance, convolutional neural network has established superior performance on large-scale image and video classification. The supervised learning (semi/weakly) methods have expressively enhanced the performance when only a small amount of annotated data is available. In addition, correlation analysis, transfer learning, multi-tasking have proven

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“…Even with microscopic inspection and complete access to reference collections, humanbased wood identification is typically accurate only to the genus level with reliable species-level identification being rare (Gasson, 2011). Machine learning, on the other hand, either alone (Martins et al, 2013;Filho et al, 2014;Barmpoutis et al, 2017;Kwon et al, 2017Kwon et al, , 2019Rosa da Silva et al, 2017;Figueroa-Mata et al, 2018;Ravindran et al, 2018Ravindran et al, , 2019Ravindran et al, , 2021de Geus et al, 2020;Hwang et al, 2020;Souza et al, 2020;Fabijańska et al, 2021) or in combination with human expertise (Esteban et al, 2009(Esteban et al, , 2017He et al, 2020), has shown promise that species-level identification might be possible, when the woods in question allow resolution at this granularity. Recent work involving the open-source XyloTron platform has shown promise for real-time, field-deployable, screening-level wood identification (Ravindran et al, 2019(Ravindran et al, , 2021Arévalo et al, 2021) with the hardware to transition to smartphone-based systems now available (Tang et al, 2018;Wiedenhoeft, 2020).…”

Section: Introductionmentioning

confidence: 99%

Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods

et al. 2022

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Availability of and access to wood identification expertise or technology is a critical component for the design and implementation of practical, enforceable strategies for effective promotion, monitoring and incentivisation of sustainable practices and conservation efforts in the forest products value chain. To address this need in the context of the multi-billion-dollar North American wood products industry 22-class, image-based, deep learning models for the macroscopic identification of North American diffuse porous hardwoods were trained for deployment on the open-source, field-deployable XyloTron platform using transverse surface images of specimens from three different xylaria and evaluated on specimens from a fourth xylarium that did not contribute training data. Analysis of the model performance, in the context of the anatomy of the woods considered, demonstrates immediate readiness of the technology developed herein for field testing in a human-in-the-loop monitoring scenario. Also proposed are strategies for training, evaluating, and advancing the state-of-the-art for developing an expansive, continental scale model for all the North American hardwoods.

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An analysis of timber sections and deep learning for wood species classification

Cited by 18 publications

References 27 publications

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

Artificial intelligence in deep learning algorithms for multimedia analysis

Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods

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