Abstract
Deep learning in recent times has become a hugely active area within machine learning. Its popularity can be attributed to the huge successes recorded by various deep learning algorithms, notably convolutional neural networks (CNNs), in various identification tasks. Deep learning algorithms are extensively used in the field of computer vision to carry out large-scale identification, and has won many image classification competitions, with performance on a par with human intelligence. CNNs can automatically learn the specific features in a problem domain by concatenating several hidden layers to produce more complex and powerful learning architectures.
The task of identifying plant species is hard enough for botanists, and virtually impossible for non-botanists. Many traditional machine learning techniques for plant identification rely on handcrafted features such as the shape, area, and perimeter of the plant, which can be very tedious to program and are prone to errors. In this chapter we propose a method for recognizing plant species using CNNs. In this method, we trained three deep CNN architectures using the concept of transfer learning, and averaged their predictions using ensemble learning. To further improve results, test time augmentation was applied to the test images, which raised the classification accuracy from 97.89% to 98.52%.
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References
Y. Lecun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
P.J. Werbos, Applications of advances in nonlinear sensitivity analysis. Syst. Model Optim, 762–770 (1982). https://doi.org/10.1007/BFb0006203
Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Proces. Syst. 25, 097–1105 (2012). https://doi.org/10.1016/j.compmedimag.2014.06.005
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A.C. Berg, L. Fei-Fei, ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
E. Aptoula, B. Yanikoglu, Morphological features for leaf based plant recognition, in 2013 IEEE International Conference on Image Processing, ICIP 2013—Proceedings, (2013), pp. 1496–1499. https://doi.org/10.1109/ICIP.2013.6738307
J. Wäldchen, P. Mäder, Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25(2), 507–543 (2018). https://doi.org/10.1007/s11831-016-9206-z
W. Rawat, Z. Wang, Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 29(9), 2352–2449 (2017). https://doi.org/10.1162/NECO_a_00990
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2323 (1998). https://doi.org/10.1109/5.726791
Y. LeCun, B. Boser, J.S. Denker, R.E. Howard, W. Habbard, L.D. Jackel, D. Henderson, Handwritten digit recognition with a back-propagation network. Dermatol. Surg. 39(1pt2), 149 (1989). https://doi.org/10.1111/dsu.12130
K. Simonyan, & A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition (2014), 1–14. doi:https://doi.org/10.1016/j.infsof.2008.09.005
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 7–12 June (2015), 1–9. doi:https://doi.org/10.1109/CVPR.2015.7298594
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, & Z. Wojna, Rethinking the Inception Architecture for Computer Vision (2015). doi:https://doi.org/10.1109/CVPR.2016.308
M.D. Zeiler, & R. Fergus, Visualizing and understanding convolutional networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8689 LNCS (Part 1), 818–833 (2014). doi:https://doi.org/10.1007/978-3-319-10590-1_53
M.D. Zeiler, & R. Fergus, Visualizing and Understanding Convolutional Networks arXiv:1311.2901v3 [cs.CV] 28 Nov 2013. Computer Vision–ECCV 2014, 8689, 818–833 (2014). doi:https://doi.org/10.1007/978-3-319-10590-1_53
G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors. IEEE Signal Process. Mag. 29(6), 82–97 (2012). https://doi.org/10.1109/MSP.2012.2205597
Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(157), 166 (1994)
M. Lin, Q. Chen, & S. Yan, Network In Network (2013) p 1–10. doi:https://doi.org/10.1109/ASRU.2015.7404828
S. Ioffe, C. Szegedy, Batch normalization: accelerating deep network training by reducing internal covariate shift Sergey. ArXiv 36(10), 800–805 (2015). https://doi.org/10.1007/s13398-014-0173-7.2
K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: Surpassing human-level performance on imagenet classification, in Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, (2015), pp. 1026–1034. https://doi.org/10.1109/ICCV.2015.123
G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in Proceedings—30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Jan, (2017), pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243
Y. Bengio, A. Courville, Deep learning of representations for unsupervised and transfer learning. Intel. Syst. Ref. Libr 49(2011), 1–28 (2013). https://doi.org/10.1007/978-3-642-36657-4_1
Plant Seedlings Classification (n.d.) Retrieved August 25, 2019, from https://www.kaggle.com/c/plant-seedlings-classification
T.M. Giselsson, R.N. Jørgensen, Jensen, P. K., M. Dyrmann, & H.S. Midtiby, A Public Image Database for Benchmark of Plant Seedling Classification Algorithms (2017). Retrieved from http://arxiv.org/abs/1711.05458
O. Ronneberger, P. Fischer, & T. Brox U-net: Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9351, 234–241 (2015). doi:https://doi.org/10.1007/978-3-319-24574-4_28
Y. LeCun, L. Bottou, G.B. Orr, K.R. Muller, Efficient BackProp. http://Yann.Lecun.Com/Exdb/Publis/Pdf/Lecun-98B.Pdf, 91(1998), 399–404 (2017)
PyTorch (n.d.) Retrieved August 25, 2019, from https://pytorch.org/
T. DeVries, & G.W. Taylor, Improved Regularization of Convolutional Neural Networks with Cutout (2017). doi:https://doi.org/10.1016/j.neuron.2007.06.026
D.P. Kingma, & J. Ba, Adam: A Method for Stochastic Optimization (2014). Retrieved from http://arxiv.org/abs/1412.6980
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Igbineweka, K., Sawyerr, B., Fasina, E. (2020). Improved Plant Species Identification Using Convolutional Neural Networks with Transfer Learning and Test Time Augmentation. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_8
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