https://scholars.lib.ntu.edu.tw/handle/123456789/605949
Title: | Identifying Fagaceae and Lauraceae species using leaf images and convolutional neural networks | Authors: | Wu T.-Y Yeh K.-T Hsu H.-C Yang C.-K Tsai M.-J YAN-FU KUO |
Keywords: | Deep learning;Leaf morphology;Plant identification;Pruning;Saliency map;artificial island;artificial neural network;dicotyledon;ecological modeling;legume;pruning;woody plant;Taiwan | Issue Date: | 2022 | Journal Volume: | 68 | Source: | Ecological Informatics | Abstract: | Lauraceae and Fagaceae are two large woody plant families that are predominant in the low- and middle-altitude regions in Taiwan. The highly interspecific similarity between some species of the family brings limitations on the management and utilization. This work proposed an approach for identifying 15 Lauraceae species and 20 Fagaceae species using leaf images and convolutional neural networks (CNNs). Leaf specimens of 35 species were collected from the northern, central, and southern parts of Taiwan. Images of the leaves were acquired using flat-bed scanners. Three CNN architectures—DenseNet-121, MobileNet V2, and Xception—were trained. Xception achieved the highest mean test accuracy of 99.39%, and MobileNet V2 required the shortest mean test time of 17.1 ms per image using a GPU. The saliency maps revealed that the characteristics learned by models matched the leaf features used by botanists. A pruning algorithm, gate decorator, was applied to the trained models for reducing the number of parameters and number of floating-point operations of the MobileNet V2 by 55.4% and 69.1%, respectively, while the model accuracy was maintained at 92.03%. Thus, MobileNet V2 has the potential to be used for identifying the Lauraceae and Fagaceae species on mobile devices. ? 2021 Elsevier B.V. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121274165&doi=10.1016%2fj.ecoinf.2021.101513&partnerID=40&md5=0753852db11ec4efa64831ecb540149e https://scholars.lib.ntu.edu.tw/handle/123456789/605949 |
ISSN: | 15749541 | DOI: | 10.1016/j.ecoinf.2021.101513 |
Appears in Collections: | 生物機電工程學系 |
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