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Identification of North American softwoods via machine-learning
- Source :
- Canadian Journal of Forest Research. 51:1245-1252
- Publication Year :
- 2021
- Publisher :
- Canadian Science Publishing, 2021.
-
Abstract
- This manuscript reports the feasibility of a sequential convolutional neural network (CNN) machine-learning model that correctly identifies 11 North American softwood species from 14× magnified macroscopic end-grain images. The convolutional network contained a large kernel size, max pooling layers, and leaky rectified linear units to accelerate training. To reduce overfitting of training data, we employed L2 regularization, custom initialization, and stratified 5-fold cross-validation techniques. The database consisted of 1789 wood end-grain images. The training data set consisted of 1431 images, whereas the validation set had approximately 358 images. In both sets, the input image size was 227 pixels × 227 pixels. Data augmentation was performed on-the-fly by flipping, rotating, and zooming the images. We tested the performance of the CNN against precision, sensitivity, specificity, F1 score, and adjusted accuracy. The adjusted accuracy for the entire model was 94.0%. Confusion matrices indicated the lowest performance was in correctly classifying ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) and eastern spruce (Picea spp. A. Dietr.) group with an average sensitivity of 89.0% for each. Even though high validation accuracy (>94.0%) was achieved, we concluded that a much larger data set is needed for wood identification to obtain industrially accurate identification of softwoods, mainly due to their visual and macroscopic similarities.
- Subjects :
- Global and Planetary Change
Softwood
Ecology
Computer science
business.industry
Forestry
04 agricultural and veterinary sciences
Machine learning
computer.software_genre
01 natural sciences
Convolutional neural network
010309 optics
Identification (information)
0103 physical sciences
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 12086037 and 00455067
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- Canadian Journal of Forest Research
- Accession number :
- edsair.doi...........b55a293e5f35a2687d2c58cb6ec0a9f8