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Open AccessTechnical Note

North American Hardwoods Identification Using Machine-Learning

1
Department of Sustainable Bioproducts/Forest and Wildlife Research Center (FWRC), Mississippi State University, Starkville, MS 39762-9820, USA
2
CAVS, Mississippi State University, Starkville, MS 39759, USA
*
Author to whom correspondence should be addressed.
Forests 2020, 11(3), 298; https://doi.org/10.3390/f11030298
Received: 11 February 2020 / Revised: 4 March 2020 / Accepted: 5 March 2020 / Published: 7 March 2020
(This article belongs to the Section Wood Science)
This technical note determines the feasibility of using an InceptionV4_ResNetV2 convolutional neural network (CNN) to correctly identify hardwood species from macroscopic images. The method is composed of a commodity smartphone fitted with a 14× macro lens for photography. The end-grains of ten different North American hardwood species were photographed to create a dataset of 1869 images. The stratified 5-fold cross-validation machine-learning method was used, in which the number of testing samples varied from 341 to 342. Data augmentation was performed on-the-fly for each training set by rotating, zooming, and flipping images. It was found that the CNN could correctly identify hardwood species based on macroscopic images of its end-grain with an adjusted accuracy of 92.60%. With the current growing of machine-learning field, this model can then be readily deployed in a mobile application for field wood identification. View Full-Text
Keywords: wood identification; machine-learning; smartphone; macro lens; Inception-ResNet; convolutional neural networks (CNN) wood identification; machine-learning; smartphone; macro lens; Inception-ResNet; convolutional neural networks (CNN)
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Verly Lopes, D.J.; Burgreen, G.W.; Entsminger, E.D. North American Hardwoods Identification Using Machine-Learning. Forests 2020, 11, 298.

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