North American Hardwoods Identification Using Machine-Learning

Forests

GregW. Burgreen¹  EdwardD. Entsminger²  DercilioJunior Verly Lopes² 

[1] CAVS, Mississippi State University, Starkville, MS 39759, USA;Department of Sustainable Bioproducts/Forest and Wildlife Research Center (FWRC), Mississippi State University, Starkville, MS 39762-9820, USA;

关键词: wood identification;    machine-learning;    smartphone;    macro lens;    inception-resnet;    convolutional neural networks (cnn);   

DOI  :  10.3390/f11030298

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown