Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Verification of a Deep Learning-Based Tree Species Identification Model Using Images of Broadleaf and Coniferous Tree Leaves
The objective of this study was to verify the accuracy of tree species identification using deep learning with leaf images of broadleaf and coniferous trees in outdoor photographs. For each of 12 broadleaf and eight coniferous tree species, we acquired 300 photographs of leaves and used those to produce 72,000 256 × 256-pixel images. We used Caffe as the deep learning framework and AlexNet and GoogLeNet as the deep learning algorithms. We constructed four learning models that combined two learning patterns: one for individual classification of 20 species and the other for two-group classification (broadleaf vs. coniferous trees), with and without data augmentation, respectively. The performance of the proposed model was evaluated according to the MCC and F-score. Both classification models exhibited very high accuracy for all learning patterns; the highest MCC was 0.997 for GoogLeNet with data augmentation. The classification accuracy was higher for broadleaf trees when the model was trained using broadleaf only; for coniferous trees, the classification accuracy was higher when the model was trained using both tree types simultaneously than when it was trained using coniferous trees only.
Verification of a Deep Learning-Based Tree Species Identification Model Using Images of Broadleaf and Coniferous Tree Leaves
The objective of this study was to verify the accuracy of tree species identification using deep learning with leaf images of broadleaf and coniferous trees in outdoor photographs. For each of 12 broadleaf and eight coniferous tree species, we acquired 300 photographs of leaves and used those to produce 72,000 256 × 256-pixel images. We used Caffe as the deep learning framework and AlexNet and GoogLeNet as the deep learning algorithms. We constructed four learning models that combined two learning patterns: one for individual classification of 20 species and the other for two-group classification (broadleaf vs. coniferous trees), with and without data augmentation, respectively. The performance of the proposed model was evaluated according to the MCC and F-score. Both classification models exhibited very high accuracy for all learning patterns; the highest MCC was 0.997 for GoogLeNet with data augmentation. The classification accuracy was higher for broadleaf trees when the model was trained using broadleaf only; for coniferous trees, the classification accuracy was higher when the model was trained using both tree types simultaneously than when it was trained using coniferous trees only.
Verification of a Deep Learning-Based Tree Species Identification Model Using Images of Broadleaf and Coniferous Tree Leaves
Yasushi Minowa (Autor:in) / Yuhsuke Kubota (Autor:in) / Shun Nakatsukasa (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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