PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK

Wulan  Dewi; Wiranto Herry Utomo

doi:10.35784/acs-2021-08

Open full text

pdf

Published: Mar 30, 2021

DOI: https://doi.org/10.35784/acs-2021-08

Issue Vol. 17 No. 1 (2021)

Articles

ASSESSMENT OF THE POSSIBILITY OF USING BAYESIAN NETS AND PETRI NETS IN THE PROCESS OF SELECTING ADDITIVE MANUFACTURING TECHNOLOGY IN A MANUFACTURING COMPANY
Marcin Topczak, Małgorzata Śliwa

5-16
PRACTICAL APPLICATION OF FUZZY LOGIC IN PRODUCTION CONTROL SYSTEMS OF ENGINEER TO ORDER SMES
Bartosz Cieśla, Janusz Mleczko

17-25
ANALYSIS OF THE POSSIBILITY OF USING MARKERS EMITTING PULSATING LIGHT IN THE TASK OF LOCALIZATION
Piotr Miś, Przemysław Szulim

26-39
RESOLUTION IN THE 3D MODELING OF OBJECTS FOR ADDITIVE MANUFACTURING AND REVERSE ENGINEERING – SHUTTER EFFECT
Elvis COUTIÑO-MORENO, Quirino ESTRADA, Daniel MALDONADO-ONOFRE, Alejandro RODRIGUEZ-MENDEZ, Julio GOMEZ-GIRON

40-52
NOVEL SIMPLE DESIGN AND ANALYSIS OF WI-MAX TRANSCEIVER USING MATLAB-SIMULINK
Muaayed F. AL-RAWI, Muhanned F. AL-RAWI

53-60
VIRTUAL REALITY IN PRODUCTION LAYOUT DESIGNING
Dariusz Plinta, Karolina Kłaptocz

61-69
SEARCH ENGINE OPTIMIZATION: A REVIEW
Firas ALMUKHTAR, Nawzad MAHMOODD, Shahab KAREEM

70-80
PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK
Wulan Dewi, Wiranto Herry Utomo

81-89

DOI

https://doi.org/10.35784/acs-2021-08

Authors

Wulan Dewi

wulandewi1517@gmail.com

President University, Faculty of Computing, Information Technology,, Indonesia

Wiranto Herry Utomo

wiranto.herry@president.ac.id

President University, Faculty of Computing, Information Technology, Indonesia

Abstract

The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%.

Keywords:

classification, leaf edges, leaf veins morphological, wavelet convolutional neural network

References

Alimboyong, C. R., & Hernandez, A. A. (2019). An Improved Deep Neural Network for Classification of Plant Seedling Images. 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA) (pp. 217–222). IEEE. https://doi.org/10.1109/CSPA.2019.8696009 DOI: https://doi.org/10.1109/CSPA.2019.8696009

Bouny, L. El., Khalil, M., & Adib, A. (2020). ECG Heartbeat Classification Based On Multi-Scale Wavelet Convolutional Neural Networks. ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3212–3216). IEEE. https://doi.org/10.1109/ ICASSP40776.2020.9054749 DOI: https://doi.org/10.1109/ICASSP40776.2020.9054749

Choi, K. S., Shin, J. S., Lee, J. J., Kim, Y. S., Kim, S. B., & Kim, C. W. (2005). Gradient-Based Learning Applied to Document Recognition. Biochemical and Biophysical Research Communications, 330(4), 1299–1305. https://doi.org/10.1016/j.bbrc.2005.03.111 DOI: https://doi.org/10.1016/j.bbrc.2005.03.111

Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR (pp. 1800–1807). IEEE. https://doi.org/10.1109/CVPR.2017.195 DOI: https://doi.org/10.1109/CVPR.2017.195

Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151(2005), 72–80. https://doi.org/10.1016/j.biosystemseng.2016.08.024 DOI: https://doi.org/10.1016/j.biosystemseng.2016.08.024

Fujieda, S., Takayama, K., & Hachisuka, T. (2018). Wavelet convolutional neural networks. In arXiv: Vol. arXiv:1805. arXiv:1805.08620.

Grinblat, G. L., Uzal, L. C., Larese, M. G., & Granitto, P. M. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418–424. https://doi.org/10.1016/j.compag.2016.07.003 DOI: https://doi.org/10.1016/j.compag.2016.07.003

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377. https://doi.org/10.1016/j.patcog.2017.10.013 DOI: https://doi.org/10.1016/j.patcog.2017.10.013

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90 DOI: https://doi.org/10.1109/CVPR.2016.90

Heredia, I. (2017). Large-scale plant classification with deep neural networks. ACM International Conference on Computing Frontiers 2017, CF 2017 (pp. 259–262). Association for Computing Machinery. https://doi.org/10.1145/3075564.3075590 DOI: https://doi.org/10.1145/3075564.3075590

Hongqiao, L., & Shengqian, W. (2009). A new image denoising method using wavelet transform. Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009 (1(1), pp. 111–114). https://doi.org/10.1109/IFITA.2009.47 DOI: https://doi.org/10.1109/IFITA.2009.47

Kimlyk, M., & Umnyashkin, S. (2018). Image Denoising Using Discrete Wavelet Transform and Edge Information. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) (pp. 1823–1825). IEEE. https://doi.org/10.1109/EIConRus.2018.8317461 DOI: https://doi.org/10.1109/EIConRus.2018.8317461

Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings (pp. 1–15). arXiv.org.

Krizhevsky, B. A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6), 84–90. DOI: https://doi.org/10.1145/3065386

Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539 DOI: https://doi.org/10.1038/nature14539

Lee, S. H., Chan, C. S., Mayo, S. J., & Remagnino, P. (2017). How deep learning extracts and learns leaf features for plant classification. Pattern Recognition, 71, 1–13. https://doi.org/10.1016/j.patcog.2017.05.015 DOI: https://doi.org/10.1016/j.patcog.2017.05.015

Lee, S. H., Chan, C. S., Wilkin, P., & Remagnino, P. (2015). Deep-plant: Plant identification with convolutional neural networks. Proceedings - International Conference on Image Processing, ICIP (pp. 452–456). https://doi.org/10.1109/ICIP.2015.7350839 DOI: https://doi.org/10.1109/ICIP.2015.7350839

Liu, J., Yang, S., Cheng, Y., & Song, Z. (2019). Plant Leaf Classification Based on Deep Learning. Proceedings 2018 Chinese Automation Congress, CAC 2018 (pp. 3165–3169). Xi'an, China. https://doi.org/10.1109/CAC.2018.8623427 DOI: https://doi.org/10.1109/CAC.2018.8623427

Liu, P., Zhang, H., Zhang, K., Lin, L., & Zuo, W. (2018). Multi-level wavelet-CNN for image restoration. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 886–895). IEEE. https://doi.org/10.1109/CVPRW.2018.00121 DOI: https://doi.org/10.1109/CVPRW.2018.00121

Mohideen, S. K., Perumal, S. A., & Sathik, M. M. (2008). Image De-noising using Discrete Wavelet transform. IJCSNS International Journal of Computer Science and Network Security, 8(1), 8–11.

Ramanarayanan, S., Murugesan, B., Ram, K., & Sivaprakasam, M. (2020). DC-WCNN: A Deep Cascade of Wavelet Based Convolutional Neural Networks for MR Image Reconstruction. Proceedings - International Symposium on Biomedical Imaging (pp. 1069–1073). IEEE. https://doi.org/10.1109/ISBI45749.2020.9098491 DOI: https://doi.org/10.1109/ISBI45749.2020.9098491

Scoffoni, C., Rawls, M., Mckown, A., Cochard, H., & Sack, L. (2011). Decline of leaf hydraulic conductance with dehydration: Relationship to leaf size and venation architecture. Plant Physiology, 156(2), 832–843. https://doi.org/10.1104/pp.111.173856 DOI: https://doi.org/10.1104/pp.111.173856

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings (pp. 1–14). CoRR, abs/1409.1556.

Song, Q., Ma, L., Cao, J., & Han, X. (2016). Image Denoising Based on Mean Filter and Wavelet Transform. Proceedings – 2015 4th International Conference on Advanced Information Technology and Sensor Application, AITS (pp. 39–42). IEEE. https://doi.org/10.1109/AITS.2015.17 DOI: https://doi.org/10.1109/AITS.2015.17

Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. 31st AAAI Conference on Artificial Intelligence (pp. 4278–4284). AAAI. DOI: https://doi.org/10.1609/aaai.v31i1.11231

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A., (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1–9). IEEE. https://doi.org/10.1109/cvpr.2015.7298594 DOI: https://doi.org/10.1109/CVPR.2015.7298594

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2818–2826). IEEE. https://doi.org/10.1109/CVPR.2016.308 DOI: https://doi.org/10.1109/CVPR.2016.308

Xie, S., Girshick, R., & Doll, P. (2017). Aggregated Residual Transformations for Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5987–5995). IEEE. https://doi.org/10.1109/CVPR.2017.634 DOI: https://doi.org/10.1109/CVPR.2017.634

Yalcin, H., & Razavi, S. (2016). Plant classification using convolutional neural networks. 2016 Fifth International Conference on Agro–Geoinformatics (Agro-Geoinformatics) (pp. 1-5). IEEE. https://doi.org/10.1109/Agro-Geoinformatics.2016.7577698 DOI: https://doi.org/10.1109/Agro-Geoinformatics.2016.7577698

Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), 1–25. https://doi.org/10.1002/widm.1253 DOI: https://doi.org/10.1002/widm.1253

Dewi, W. ., & Utomo, W. H. (2021). PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK. Applied Computer Science, 17(1), 81–89. https://doi.org/10.35784/acs-2021-08

PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK

Issue Vol. 17 No. 1 (2021)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 17 No. 1 (2021)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License