Automatic classification of native wood charcoal

Maruyama, Teruo Matos; Oliveira, Luiz S.; Britto, Alceu de Souza; Nisgoski, Silvana

doi:10.1016/j.ecoinf.2018.05.008

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Both species have solitary and radial multiples pores that are large to very large in size as well as scanty, vasicentric and marginal parenchyma. Maruyama et al (2018) also reported misclassification while identifying microscopic images of hardwood charcoal. Species that had similar attributes were assigned to the wrong category.…”

Section: Discussionmentioning

confidence: 99%

Microscopic identification of brazilian commercial wood species via machine-learning

Moulin

Lopes

Mulin

et al. 2022

CERNE

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Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 commercially available Brazilian wood species using a custom DCNN model. Results:Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model's robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion:The custom machine learned model accuracy was higher than 0.90. The model's worst performance was identified in distinguishing between Toona ciliata and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security.

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Section: Discussionmentioning

confidence: 99%

Microscopic identification of brazilian commercial wood species via machine-learning

Moulin

Lopes

Mulin

et al. 2022

CERNE

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“…So, we used bulk food grains images for this study. we used the grain seeds images database provided from The Laboratory of Vision, Robotics and Imaging (VRI) in the Federal University of Parana (UFPR ) [19][20][21]. The image dataset has 339 images from 13 distinct seeds species.…”

Section: Datasetmentioning

confidence: 99%

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Ali¹,

Mohammed²,

Nour³

2021

Kafrelsheikh Journal of Information Sciences

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The goal of this research is to compare between the performance of the traditional machine learning classification algorithm using Bag of Visual Words (BoVW) method and off-the-shelf deep features extracted by VGG-19, and Inception-V3 models and trained SVMs using the extracted features. By comparing the AUC, sensitivity, and specificity of SVM with VGG-19 and Inception-V3, we can conclude that off-the-shelf deep features has an important impact on food grains image classification.

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“…In palaeolimnology, Racca et al (2003) use a multi-layer perceptron neural network to reduce diatom taxa for calibration purposes. Maruyama et al (2018) use a convolutional neural network as a feature extractor to identify the species of native wood charcoal pieces, created from samples of modern trees. Weller et al (2007) apply a supervised neural network to identification of sedimentary organic matter within pollen slides.…”

Section: Background and Related Workmentioning

confidence: 99%

Fossil charcoal particle identification and classification by two convolutional neural networks

Rehn

Possemiers

2019

Quaternary Science Reviews

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Automatic classification of native wood charcoal

Cited by 13 publications

References 20 publications

Microscopic identification of brazilian commercial wood species via machine-learning

Microscopic identification of brazilian commercial wood species via machine-learning

Product Based Classification of Bulk Food Grains using Bag of Visual Words and Deep Features

Fossil charcoal particle identification and classification by two convolutional neural networks

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