Abstract
Recently, many researchers have been inspired by the success of deep learning in computer vision to improve the performance of detection systems for plant diseases. Unfortunately, most of these studies did not leverage recent deep architectures and were based essentially on AlexNet, GoogleNet or similar architectures. Moreover, the research did not take advantage of deep learning visualisation methods which qualifies these deep classifiers as black boxes as they are not transparent. In this chapter, we have tested multiple state-of-the-art Convolutional Neural Network (CNN) architectures using three learning strategies on a public dataset for plant diseases classification. These new architectures outperform the state-of-the-art results of plant diseases classification with an accuracy reaching 99.76%. Furthermore, we have proposed the use of saliency maps as a visualisation method to understand and interpret the CNN classification mechanism. This visualisation method increases the transparency of deep learning models and gives more insight into the symptoms of plant diseases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Images are randomly cropped to be 299 \(*\) 299 for Inception v3 architecture and 224 \(*\) 224 for (AlexNet, DenseNet-169, ResNet-34, SqueezeNet-1.1 and VGG13).
- 2.
References
Akhtar, A., Khanum, A., Khan, S.A., Shaukat, A.: Automated plant disease analysis (APDA): performance comparison of machine learning techniques. In: 2013 11th International Conference on Frontiers of Information Technology, pp. 60–65. IEEE Computer Society, Islamabad (2013)
Al Hiary, H., Bani Ahmad, S., Reyalat, M., Braik, M., ALRahamneh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011)
Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.: AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35(5), 1313–1321 (2016). https://doi.org/10.1109/TMI.2016.2528120
Blancard, D.: 2 - Diagnosis of Parasitic and Nonparasitic Diseases. Academic Press, The Netherlands (2012)
Brahimi, M., Boukhalfa, K., Moussaoui, A.: Deep learning for tomato diseases: classification and symptoms visualization. Appl. Artif. Intell. 31(4), 1–17 (2017)
Chen, X.W., Lin, X.: Big data deep learning: challenges and perspectives. IEEE Access 2, 514–525 (2014). https://doi.org/10.1109/ACCESS.2014.2325029
Dandawate, Y., Kokare, R.: An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. In: 2015 International Conference on Advances in Computing. Communications and Informatics, ICACCI 2015, pp. 794–799. IEEE, Kochi, India (2015)
DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E.L., Yosinski, J., Gore, M.A., Nelson, R.J., Lipson, H.: Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology (2017). https://doi.org/10.1094/PHYTO-11-16-0417-R
Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H.: Basic investigation on a robust and practical plant diagnostic system. In: Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, pp. 989–992 (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1–8. IEEE (2009)
Hanssen, I.M., Lapidot, M.: Major tomato viruses in the Mediterranean basin. Adv. Virus Res. 84, 31–66 (2012)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR arXiv:1512.03385 (2015)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. CoRR arXiv:1608.0 (2016)
Hughes, D., Salathe, M.: An open access repository of images on plant health to enable the development of mobile disease diagnostics, pp. 1–13 (2015)
Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. CoRR arXiv:1602.07360 (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, MM ’14, pp. 675–678. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2647868.2654889
Kawasaki, Y., Uga, H., Kagiwada, S., Iyatomi, H.: Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, 14–16 December 2015, Proceedings, Part II, pp. 638–645 (2015)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR arXiv:1609.02907 (2016)
Koike, S.T., Gladders, P., Paulus, A.O.: Vegetable Diseases: A Color Handbook. Academic Press, San Diego (2007)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lee, W., Kim, S., Lee, Y.T., Lee, H.W., Choi, M.: Deep neural networks for wild fire detection with unmanned aerial vehicle. In: 2017 IEEE International Conference on Consumer Electronics (ICCE), pp. 252–253 (2017)
Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7(September), 1–7 (2016)
Mokhtar, U., El-Bendary, N., Hassenian, A.E., Emary, E., Mahmoud, M.A., Hefny, H., Tolba, M.F., Mokhtar, U., Hassenian, A.E., Emary, E., Mahmoud, M.A.: SVM-Based detection of tomato leaves diseases. In: Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P., Zadrozny, S. (eds.) Advances in Intelligent Systems and Computing, vol. 323, pp. 641–652. Springer, Cham (2015)
Nachtigall, L.G., Araujo, R.M., Nachtigall, G.R.: Classification of apple tree disorders using convolutional neural networks. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 472–476 (2016). https://doi.org/10.1109/ICTAI.2016.0078
Otálora, S., Perdomo, O., González, F., Müller, H.: Training Deep Convolutional Neural Networks with Active Learning for Exudate Classification in Eye Fundus Images, pp. 146–154. Springer International Publishing, Cham (2017)
Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1742–1750 (2015)
Sharma, M., Saha, O., Sriraman, A., Hebbalaguppe, R., Vig, L., Karande, S.: Crowdsourcing for chromosome segmentation and deep classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 786–793 (2017). https://doi.org/10.1109/CVPRW.2017.109
Shinozaki, T.: Semi-supervised Learning for Convolutional Neural Networks Using Mild Supervisory Signals, pp. 381–388. Springer International Publishing, Cham (2016)
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. CoRR arXiv:1312.6034 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556 (2014)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016 (2016)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. CoRR arXiv:1412.6806 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07–12 June, Boston, USA, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826. arXiv:1512.0 (2015)
Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. CoRR arXiv:1701.03551 (2017)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. LNCS, vol. 8689 (PART 1), pp. 818–833 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., Moussaoui, A. (2018). Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-90403-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-90402-3
Online ISBN: 978-3-319-90403-0
eBook Packages: Computer ScienceComputer Science (R0)