期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 17, 期 1, 页码 82-90出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2018.2848653
关键词
Feature extraction; Veins; Machine learning; Image color analysis; Shape; Support vector machines; Artificial neural networks; Tropical tree; deep learning; convolutional network; leaf vein morphometric; feature extraction; classification; artificial neural network
类别
资金
- University of Malaya Research Grant (UMRG) [RP038C-15AET]
An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naive-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.
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