Automatic Plant Image Identification of Vietnamese species using Deep Learning Models
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International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2020 by IJETT Journal | ||
| Volume-68 Issue-4 |
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| Year of Publication : 2020 | ||
| Authors : Nguyen Van Hieu, Ngo Le Huy Hien |
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| DOI : 10.14445/22315381/IJETT-V68I4P205S |
Citation
MLA Style: Nguyen Van Hieu, Ngo Le Huy Hien "Automatic Plant Image Identification of Vietnamese species using Deep Learning Models" International Journal of Engineering Trends and Technology 68.4(2020):25-31.
APA Style:Nguyen Van Hieu, Ngo Le Huy Hien. Automatic Plant Image Identification of Vietnamese species using Deep Learning Models International Journal of Engineering Trends and Technology, 68(4),25-31.
Abstract
It is complicated to distinguish among thousands of plant species in the natural ecosystem, and many efforts have been investigated to address the issue. In Vietnam, the task of identifying one from 12,000 species requires specialized experts in flora management, with thorough training skills and in-depth knowledge. Therefore, with the advance of machine learning, automatic plant identification systems have been proposed to benefit various stakeholders, including botanists, pharmaceutical laboratories, taxonomists, forestry services, and organizations. The concept has fueled an interest in research and application from global researchers and engineers in both fields of machine learning and computer vision. In this paper, the Vietnamese plant image dataset was collected from an online encyclopedia of Vietnamese organisms, together with the Encyclopedia of Life, to generate a total of 28,046 environmental images of 109 plant species in Vietnam. A comparative evaluation of four deep convolutional feature extraction models, which are MobileNetV2, VGG16, ResnetV2, and Inception Resnet V2, is presented. Those models have been tested on the Support Vector Machine (SVM) classifier to experiment with the purpose of plant image identification. The proposed models achieve promising recognition rates, and MobilenetV2 attained the highest with 83.9%. This result demonstrates that machine learning models are potential for plant species identification in the natural environment, and future works need to examine proposing higher accuracy systems on a larger dataset to meet the current application demand.
Reference
[1] N. Kumar et al., “Leaf snap: A Computer vision system for automatic plant species identification”, Proc. Computer Vision – ECCV 2012, Springer, Berlin, Heidelberg, 2012, pp. 502–516.
[2] Du, M., Zhang, S. and Wang, H., “Supervised Isomap for Plant Leaf Image Classification”, 5th International Conference on Emerging Intelligent Computing Technology and Applications, pp. 627-634, 2009
[3] Hossain, J. and Amin, M.A., “Leaf Shape Identification Based Plant Biometrics”, 13th International Conference on Computer and Information Technology, Dhaka, Bangladesh, pp. 458-463, 2010.
[4] Du, J.X., Wang, X.F. and Zhang, G.J., “Leaf shape-based plant species recognition”, Applied Mathematics and Computation, 2007.
[5] Y. Nam and E. Hwang, “A representation and matching method for shape-based leaf image retrieval”, Journal of KIISE: Software and Applications, vol. 32, no. 11, pp. 1013-1021, 2005.
[6] Y. Nam, J. Park, E. Hwang, and D. Kim, “Shape-based leaf image retrieval using venation feature”, Proceedings of 2006 Korea Computer Congress, vol. 33, no. 1D, pp. 346-348, 2006.
[7] A. Aakif and M. F. Khan, “Automatic classification of plants based on their leaves”, Biosystems Engineering, vol. 139, pp. 66–75, 2015.
[8] Wang-Su Jeon and Sang-Yong Rhee, “Plant Leaf Recognition Using a Convolution Neural Network”, International Journal of Fuzzy Logic and Intelligent Systems, Vol. 17, No. 1, pp. 26-34, March 2017.
[9] Carranza-Rojas, J. and Mata-Montero, E., “Combining Leaf Shape and Texture for Costa Rican Plant Species Identification”, CLEI Electronic Journal, 19(1), pp. 7, 2016.
[10] G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, “Deep learning for plant identification using vein morphological patterns”, Computers and Electronics in Agriculture, pp. 418–424, 2016.
[11] Y. Sun, Y. Liu, G. Wang, and H. Zhang, “Deep learning for plant identification in natural environment”, Computer Intel Neurosis, 2017.
[12] Du, J.X., Wang, X.F. and Zhang, G.J., “Leaf shape-based plant species recognition”, Applied Mathematics and Computation, 2007.
[13] Du, M., Zhang, S. and Wang, H., “Supervised Isomap for Plant Leaf Image Classification”, 5th International Conference on Emerging Intelligent Computing Technology and Applications, Ulsan, South Korea, pp. 627-634, 2009.
[14] Du, J.X., Zhai, C.M. and Wang, Q.P., “Recognition of plant leaf image based on fractal dimension features”, Neurocomputing, 2013.
[15] Yu Sun, Yuan Liu, Guan Wang, Haiyan Zhang, “Deep Learning for Plant Identification in Natural Environment”, Computational Intelligence and Neuroscience, May 2017.
[16] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510-4520.
[17] Mark S., and Andrew H., “MobileNetV2: The Next Generation of On-Device Computer Vision Networks”, Google Research, April 2018.
[18] Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, ICLR, Apr 2015.
[19] Muneeb ul H., VGG16 – Convolutional Network for Classification and Detection, Neurohive, Nov 2018.
[20] Nair V., Hinton G.E., “Rectified linear units improve restricted boltzmann machines”, ICML, 2010.
[21] Lin T.Y. et al., Microsoft COCO: Common objects in context, ECCV, 2014.
[22] Yu S., Yuan L., Guan W., and Haiyan Z., “Deep Learning for Plant Identification in Natural Environment”, Computational Intelligence and Neuroscience, May 2017.
[23] He K., Zhang X., Ren S., and Sun J., “Deep residual learning for image recognition”, CVPR, 2016.
[24] Packt, ResNet v2, “Big Data and Business Intelligence”, Available: https://subscription.packtpub.com/, 2020.
[25] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition”, Microsoft Research, Dec 2015.
[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Identity Mappings in Deep Residual Networks”, Microsoft Research, Jul 2016.
[27] Sophia C., Kiat J.H., Teck W.C., MD Abdullah A. M., and Yang L.C., “Plant Identification on Amazonian and Guiana Shield Flora: NEUON”, in press, Life CLEF 2019 Plant, 2019.
[28] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi, Inception-v4, “Inception-ResNet and the Impact of Residual Connections on Learning”, Microsoft Research, Aug 2016.
[29] Sandler, M., Howard, A., Zhu, M., and A. Chen L.C., “Mobilenetv2: Inverted residuals and linear bottlenecks, in press”, IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 4510–4520, June 2018.
[30] Kaiming H., Xiangyu Z., Shaoqing R., and Jian S., “Deep Residual Learning for Image Recognition”, Microsoft Research, Dec 2015.
[31] Ribeiro B., “Support vector machines for quality monitoring in a plastic injection molding process”, IEEE Transactions on Systems, Man and Cybernetics C., pp. 401–410, 2005.
[32] Song Q, Hu W, and Xie W., “Robust support vector machine with bullet hole image classification”, IEEE Transactions on Systems, Man and Cybernetics C., pp. 440–448, 2002.
[33] Phung My Trung et al., “Encyclopedia of Vietnamese Creatures”, Available: vncreatures.net.
[34] Smithsonian Institution’s National Museum of Natural History, Encyclopedia of Life, Available: https://eol.org/.
[35] Marc-Olivier Arsenault, Lossless Triplet loss, Towards Data Science, Feb 2018, Available: https://towardsdatascience.com/lossless-triplet-loss-7e932f990b24.
Keywords
Plant identification, convolutional neural network, support vector machine, deep learning models.