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.

中文翻译：

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使用叶脉形态计量学对植物物种分类进行深度学习。

自动化的植物物种识别系统可以帮助植物学家和外行快速识别植物物种。深度学习在特征提取方面非常强大，因为它在提供更深层的图像信息方面表现出色。在这项研究中，提出了一种新的基于CNN的方法D-Leaf。对叶片图像进行预处理，并使用三种不同的卷积神经网络（CNN）模型（即经过预先训练的AlexNet，经过微调的AlexNet和D-Leaf）提取特征。然后使用五种机器学习技术对这些功能进行分类，这些技术分别是支持向量机（SVM），人工神经网络（ANN），k最近邻（k-NN），朴素贝叶斯（NB）和CNN。常规的形态计量方法基于Sobel分段静脉计算形态测量值，用于基准测试。与AlexNet（93.26％）和经过微调的AlexNet（95.54％）模型相比，D-Leaf模型达到了可比较的测试精度94.88％。此外，CNN模型的性能优于传统形态测量（66.55％）。发现从CNN中提取的特征与ANN分类器非常吻合。如实验结果所示，D-Leaf可以是一种有效的用于植物物种识别的自动化系统。