Journal
FORESTS
Volume 11, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/f11030298
Keywords
wood identification; machine-learning; smartphone; macro lens; Inception-ResNet; convolutional neural networks (CNN)
Categories
Funding
- U.S. Department of Agriculture (USDA), Research, Education, and Economics (REE), Agriculture Research Service (ARS)
- Administrative and Financial Management (AFM)
- Financial Management and Accounting Division (FMAD)
- Grants and Agreements Management Branch (GAMB) [58-0204-9-164]
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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 14x 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.
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