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- W3009133068 abstract "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." @default.
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- W3009133068 date "2020-03-07" @default.
- W3009133068 modified "2023-09-25" @default.
- W3009133068 title "North American Hardwoods Identification Using Machine-Learning" @default.
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- W3009133068 doi "https://doi.org/10.3390/f11030298" @default.
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