IEEE/ACM Trans Comput Biol Bioinform. 2020 Jan-Feb;17(1):82-90. doi: 10.1109/TCBB.2018.2848653. Epub 2018 Jun 19.
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), Naïve-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.
一个自动化的植物物种识别系统可以帮助植物学家和外行人快速识别植物物种。深度学习在特征提取方面很强大，因为它在提供图像的更深层次信息方面更有优势。在这项研究中，提出了一种名为 D-Leaf 的新基于 CNN 的方法。通过使用三种不同的卷积神经网络（CNN）模型，即预训练的 AlexNet、微调的 AlexNet 和 D-Leaf，对叶片图像进行预处理并提取特征。然后，使用五种机器学习技术，即支持向量机（SVM）、人工神经网络（ANN）、k-最近邻（k-NN）、朴素贝叶斯（NB）和 CNN 对这些特征进行分类。传统的形态测量方法根据 Sobel 分割的叶脉计算形态测量值，作为基准。D-Leaf 模型的测试准确率为 94.88%，与 AlexNet（93.26%）和微调 AlexNet（95.54%）模型相当。此外，CNN 模型的性能优于传统的形态测量值（66.55%）。从 CNN 中提取的特征与 ANN 分类器拟合良好。实验结果表明，D-Leaf 可以成为一种有效的植物物种识别自动化系统。
