Abstract
As the latest breakthrough in the field of computer vision, deep convolutional neural network(CNN) is very promising for the classification of crop diseases. However, the common limitation applying the algorithm is reliance on a large amount of training data. In some cases, obtaining and labeling a large dataset might be difficult. We solve this problem both from the network size and the training mechanism. In this paper, using 2430 images from the natural environment, which contain 2 crop species and 8 diseases, 6 kinds of CNN with different depths are trained to investigate appropriate structure. In order to address the over-fitting problem caused by our small-scale dataset, we systemically analyze the performances of training from scratch and using transfer learning. In case of transfer learning, we first train PlantVillage dataset to get a pre-trained model, and then retrain our dataset based on this model to adjust parameters. The CNN with 5 convolutional layers achieves an accuracy of 90.84% by using transfer learning. Experimental results demonstrate that the combination of CNN and transfer learning is effective for crop disease images classification with small-scale dataset.
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References
Zhang, S.W., Shang, Y.J., Wang, L.: Plant disease recognition based on plant leaf image. J. Anim. Plant Sci. 25(3), 42–45 (2015)
Rumpf, T., Mahlein, A.K., Steiner, U., Oerke, E.C., Dehne, H.W., Plmer, L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74(1), 91–99 (2010)
Kai, S., Liu, Z., Hang, S., Guo, C.: A research of maize disease image recognition of corn based on BP networks. In: Third International Conference on Measuring Technology and Mechatronics Automation, pp. 246–249 (2011)
Sammany, M., Medhat, T.: Dimensionality reduction using rough set approach for two neural networks-based applications. In: International Conference on Rough Sets and Intelligent Systems Paradigms, pp. 639–647 (2007)
Wan, J., Wang, D., Hoi, S.C.H., Wu, P., Zhu, J., Zhang, Y., Li, J.: Deep learning for content-based image retrieval:a comprehensive study. In: the ACM International Conference, pp. 157–166 (2014)
Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Sig. Process. 7(3), 197–387 (2014)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Srdjan, S., Marko, A., Andras, A., Dubravko, C., Darko, S.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016(6), 1–11 (2016)
Mohanty, S.P., Hughes, D.P., Salath, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. In: Datenbanksysteme Fr Business, Technologie Und Web, pp. 79–88 (2017)
Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2017, 2917536 (2017)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255 (2009)
Massini, M., Fortunato, M., Mancini, S., Tombesi, P.: L2 regularization for learning kernels, vol. 62, no. (4), pp. 109–116 (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. Comput. Sci. 3(4), 212–223 (2012)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)
Acknowledgments
The work is partially supported by the National Natural Science Foundation of China (31501223) and Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture, P.R. China.
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Wang, J., Chen, L., Zhang, J., Yuan, Y., Li, M., Zeng, W. (2018). CNN Transfer Learning for Automatic Image-Based Classification of Crop Disease. In: Wang, Y., Jiang, Z., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2018. Communications in Computer and Information Science, vol 875. Springer, Singapore. https://doi.org/10.1007/978-981-13-1702-6_32
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DOI: https://doi.org/10.1007/978-981-13-1702-6_32
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