A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification
A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification
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具有手工特征和非手工特征的植物物种自动识别深度学习模型的比较研究
DOI：
10.4018/ijaeis.2020040104
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Shamik Tiwari
中科院分区：
文献类型：
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作者：
Shamik Tiwari
植物的分类是植物学家最重要的目标之一，因为植物在自然生活史中占有重要地位。在这项工作中，叶为基础的自动植物分类框架进行了研究。目的是比较两种不同的深度学习方法，即深度神经网络（DNN）和深度卷积神经网络（CNN）。在深度神经网络的情况下，混合形状和纹理特征被用作手工特征，而在卷积非手工的情况下，特征被应用于分类。所提供的框架与公共叶数据库进行评估。从仿真结果中，证实了基于深度CNN的深度学习框架比基于手工特征的方法表现出上级分类性能。
The classification of plants is one of the most important aims for botanists since plants have a significant part in the natural life cycle. In this work, a leaf-based automatic plant classification framework is investigated. The aim is to compare two different deep learning approaches named Deep Neural Network (DNN) and deep Convolutional Neural Network (CNN). In the case of deep neural network, hybrid shapes and texture features are utilized as hand-crafted features while in the case of the convolution non-handcraft, features are applied for classification. The offered frameworks are evaluated with a public leaf database. From the simulation results, it is confirmed that the deep CNN-based deep learning framework demonstrates superior classification performance than the handcraft feature based approach.