An automatic recognition system of Brazilian flora species based on textural features of macroscopic images of wood

Souza, Deivison Venicio; Santos, Joielan Xipaia dos; Vieira, Helena Cristina; Naide, Tawani Lorena; Nisgoski, Silvana; Oliveira, Luiz S.

doi:10.1007/s00226-020-01196-z

Cited by 30 publications

(24 citation statements)

References 39 publications

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“…(2020). Attempts to reduce the need for human expertise are being made using automated machine learning (Andrade et al., 2020; He, et al., 2020; He et al., 2020; Hermanson et al., 2019; Hermanson & Wiedenhoeft, 2011; Ibrahim et al., 2017; Olschofsky & Kohl, 2020; Paula Filho et al., 2014; Ravindran et al., 2020; Souza et al., 2020; Wang et al., 2013; Yusof et al., 2013). This is clearly a rapidly moving field of study.…”

Section: Methods Used For Identificationmentioning

confidence: 99%

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WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber

Gasson

Lancaster

Young³

et al. 2020

Plants People Planet

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Section: Methods Used For Identificationmentioning

confidence: 99%

“…Attempts to reduce the need for human expertise are being made Paula Filho et al, 2014;Ravindran et al, 2020;Souza et al, 2020;Wang et al, 2013;Yusof et al, 2013). This is clearly a rapidly moving field of study.…”

Section: Me Thods Us Ed For Identifi C Ati Onmentioning

confidence: 99%

WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber

Gasson

Lancaster

Young³

et al. 2020

Plants People Planet

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show abstract

“…Simple tasks such as grayscale conversion and image cropping and scaling are preprocessing techniques commonly used in the conventional ML models [ 26 – 28 , 52 , 62 , 71 , 95 ]. The color of wood generally is not regarded as important information in because it is easily changed by various factors.…”

Section: Preprocessingmentioning

confidence: 99%

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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“…The feature extraction methods contain border detection algorithm, Gray Level Co-occurrence Matrix [5,6], and color hist statistical method [7], etc. The classification methods contain support vector machine (SVM) [2,8], KNN [9][10][11], neural network [12][13][14], etc.…”

Section: Problem Definition and Related Workmentioning

confidence: 99%

Wood Species Recognition with Small Data: A Deep Learning Approach

Sun¹,

Lin²,

He³

et al. 2021

IJCIS

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Wood species recognition is an important work in the wood trade and wood commercial activities. Although many recognition methods were presented in recent years, the existing wood species recognition methods mainly use shallow recognition models with low accuracy and are still unsatisfying for many real-world applications. Besides, their generalization ability is not strong. In this paper, a novel deep-learning-based wood species recognition method was proposed, which improved the accuracy and generalization greatly. The method uses 20X amplifying glass to acquire wood images, extracts the image features with ResNet50 neural network, refines the features with linear discriminant analysis (LDA), and recognizes the wood species with a KNN classifier. Our data was small, but we adopted transfer learning to improve our method. About 3000 wood images were used in our wood species recognition experiments and our method was executed in 25 rare wood species and the results showed our method had better generalization performance and accuracy. Compared with traditional deep learning our results were obtained from a small amount of data, which just confirmed the effectiveness of our method.

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An automatic recognition system of Brazilian flora species based on textural features of macroscopic images of wood

Cited by 30 publications

References 39 publications

WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber

WorldForestID: Addressing the need for standardized wood reference collections to support authentication analysis technologies; a way forward for checking the origin and identity of traded timber

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

Wood Species Recognition with Small Data: A Deep Learning Approach

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