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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

Shamik Tiwari
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 14
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781799806943|DOI: 10.4018/IJAEIS.2020040104
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MLA

Tiwari, Shamik. "A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification." IJAEIS vol.11, no.2 2020: pp.44-57. http://doi.org/10.4018/IJAEIS.2020040104

APA

Tiwari, S. (2020). A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 11(2), 44-57. http://doi.org/10.4018/IJAEIS.2020040104

Chicago

Tiwari, Shamik. "A Comparative Study of Deep Learning Models With Handcraft Features and Non-Handcraft Features for Automatic Plant Species Identification," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 11, no.2: 44-57. http://doi.org/10.4018/IJAEIS.2020040104

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Abstract

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.