Experimental evaluation of Data Augmentation heuristics for plant identification systems based on Deep Learning
ResumoData augmentation (DA) allows increasing datasets for training machine learning models that demands large amounts of data. In real-world applications in which data may not be abundant enough and data acquisition is not easy, DA enables increasing diversity and introducing model generalization. In this work we evaluate several DA techniques and combining approaches to extend image datasets used to train plant species recognition models. We experimentally validated Deep Convolutional Neural Networks (DCNN) with several datasets obtained from common augmentation techniques and combinations. The results allowed the identification of the Translate + Crop augmentation policy as the most effective within the scope of evaluation.
Buda, M., Maki, A., e Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106:249 – 259. DOI: https://doi.org/10.1016/j.neunet.2018.07.011
Goëau, H., Bonnet, P., e Joly, A. (2018). Overview of expertlifeclef 2018: how far automated identification systems are from the best experts?
Goëau, H., Joly, A., Bonnet, P., Bakic, V., Barthélémy, D., Boujemaa, N., e Molino, J.-F. (2013). The imageCLEF Plant Identification Task 2013. Proceedings of the 2nd ACM International Workshop on Multimedia Analysis for Ecological Data, (i):23–28. DOI: https://doi.org/10.1145/2509896.2509902
Haupt, J., Kahl, S., Kowerko, D., e Eibl, M. (2018). Large-scale plant classification using deep convolutional neural networks. In CLEF (Working Notes).
He, K., Zhang, X., Ren, S., e Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., e Saba, T. (2018). Ccdf: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep cnn features. Computers and electronics in agriculture, 155:220–236. DOI: https://doi.org/10.1016/j.compag.2018.10.013
Kingma, D. P. e Ba, J. (2017). Adam: A method for stochastic optimization.
Mehdipour Ghazi, M., Yanikoglu, B., e Aptoula, E. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing. DOI: https://doi.org/10.1016/j.neucom.2017.01.018
Pandian, J. A., Geetharamani, G., e Annette, B. (2019). Data augmentation on plant leaf disease image dataset using image manipulation and deep learning techniques. In 2019 IEEE 9th International Conference on Advanced Computing (IACC), pages 199–204. IEEE. DOI: https://doi.org/10.1109/IACC48062.2019.8971580
Pawara, P., Okafor, E., Schomaker, L., e Wiering, M. (2017). Data augmentation for plant classification. In International Conference on Advanced Concepts for Intelligent Vision Systems, pages 615–626. Springer. DOI: https://doi.org/10.1007/978-3-319-70353-4_52
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., e Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252.
Shorten, C. e Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60.
Sulc, M., Picek, L., e Matas, J. (2018). Plant recognition by inception networks with test-time class prior estimation. In CLEF (Working Notes).
Taylor, L. e Nitschke, G. (2018). Improving deep learning with generic data augmentation. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1542–1547. IEEE.
Wang, J., Perez, L., et al. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11.
Zhang, C., Zhou, P., Li, C., e Liu, L. (2015). A convolutional neural network for leaves recognition using data augmentation. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pages 2143–2150. IEEE.
Zhao, B., Li, J., Baenziger, P. S., Belamkar, V., Ge, Y., Zhang, J., e Shi, Y. (2020). Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management. Agronomy, 10(11).