PLANT CLASSIFICATION BASED ON LEAF EDGES AND LEAF MORPHOLOGICAL VEINS USING WAVELET CONVOLUTIONAL NEURAL NETWORK
Wulan Dewi
wulandewi1517@gmail.comPresident University, Faculty of Computing, Information Technology, (Indonesia)
Wiranto Herry Utomo
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 networkReferences
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Fujieda, S., Takayama, K., & Hachisuka, T. (2018). Wavelet convolutional neural networks. In arXiv: Vol. arXiv:1805. arXiv:1805.08620.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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.
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
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
Google Scholar
Authors
Wulan Dewiwulandewi1517@gmail.com
President University, Faculty of Computing, Information Technology, Indonesia
Authors
Wiranto Herry UtomoPresident University, Faculty of Computing, Information Technology Indonesia
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