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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

DCGAN-based deep learning approach for medicinal leaf identification

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pp. 717–723Vol. 44Issue 4May 2023DOI: 10.47974/JIOS-1270XML
Received:
02 Sep 2022
Published Online:
28 Aug 2023
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1270
Pages:
717–723

Abstract

Though visual identification of plants seems easier for the trained botanists or agriculturists, the automated identification of plants using leaf images still remains a challenging task. The proper identification of plants forms the most important phase as it leads to usage of plants for various purposes. In this paper, we have manually collected about 30 leaves per species belonging to five medicinal plant species. The dataset was created using the scans of the adaxial and abaxial sides of the leaves. As the small number of images makes it difficult for the Convolutional neural network to learn the features, we have augmented the dataset using Deep Convolutional Generative Adversarial Networks (DCGAN). This paper shows that the low-quality images obtained by the scanner could be effectively augmented using the DCGAN thus increasing the variance in the dataset. A comparison of proposed versions of deep learning models namely VGG16, ResNet50 and DenseNet 121 is presented. To validate the results obtained, 5-Fold-Cross validation was used. 

Keywords

DCGANDeep learningMedicinal leavesConvolutional neural networks

Subject Classifications

68T07

References

[1] N. Mamedov, “Medicinal Plants Studies: History, Challenges and Prospective,” Med. Aromat. Plants, vol. 01, no. 08, 2012, doi: 10.4172/2167-0412.1000e133.
[2] B. M. Joshi and H. Bhavsar, “Plant leaf disease detection and control: A survey,” J. Inf. Optim. Sci., vol. 41, no. 2, pp. 475–487, 2020, doi: 10.1080/02522667.2020.1734295.
[3] A. Dureja and P. Pahwa, “Medical image retrieval for detecting pneumonia using binary classification with deep convolutional neural networks,” J. Inf. Optim. Sci., vol. 41, no. 6, pp. 1419–1431, 2020, doi: 10.1080/02522667.2020.1809096.
[4] D. Virmani, C. Gupta, P. Bamdev, and P. Jain, “iSeePlus : A cost effective smart assistance archetype based on deep learning model for visually impaired,” J. Inf. Optim. Sci., vol. 41, no. 7, pp. 1741–1756, 2020, doi: 10.1080/02522667.2020.1812198.
[5] V. Gupta, S. Juyal, G. P. Singh, C. Killa, and N. Gupta, “Emotion recognition of audio/speech data using deep learning approaches,” J. Inf. Optim. Sci., vol. 41, no. 6, pp. 1309–1317, 2020, doi: 10.1080/02522667.2020.1809089.
[6] J. Hu, Z. Chen, M. Yang, R. Zhang, and Y. Cui, “A multiscale fusion convolutional neural network for plant leaf recognition,” IEEE Signal Process. Lett., vol. 25, no. 6, pp. 853–857, Jun. 2018, doi: 10.1109/LSP.2018.2809688.
[7] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.
[8] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-December, doi: 10.1109/CVPR.2016.90.
[9] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: 10.1109/CVPR.2017.243.

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