Abstract
Currently, research on leaf disease detection using deep convolutional neural networks (CNNs) requires a huge amount of data but it is difficult to obtain. So, in this paper, we have chosen to apply a more efficient technique to solve this problem. Increasing data plays a crucial role in increasing the number of images in our dataset due to efficiently and comprehensively detect plant diseases on leaves which often help improve performance of our (CNN) model and reduce over-fitting. Our best-found model is trained first on our own dataset of original images and augmented with data for leaf detection of diseased or healthy plants. We are preparing the usefulness of certain DA techniques (rotation, blur, contrast, scaling, illumination and projective transformation). Our obtained results show that our CNN model with augmented or synthetic data sets gives a higher precision, which also outperforms the result without (DA).We evaluate the utility of technique (DA), the developed system achieves better detection performance than those proposed in the state of the art. The disease detection model recorded a confidence score of 94.80% while with the data augmentation (DA) technique produces 97.2% accuracy and displays an error rate of 6.3% in real time. Finally, to compare their performance, we use the implementation under Anaconda 2019.10.
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References
Kobayashi, K., Tsuji, J., Noto, M.: Evaluation of data augmentation for image-based plant-disease detection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2206–2211. IEEE, October 2018
Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)
Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett. 24(3), 279–283 (2017)
Sato, I., Nishimura, H., Yokoi, K.: Apac: Augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229 (2015)
Pawara, P., Okafor, E., Schomaker, L., Wiering, M.: Data augmentation for plant classification. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 615–626. Springer, Cham, September 2017
Munisami, T., Ramsurn, M., Kishnah, S., Pudaruth, S.: Plant leaf recognition using shape features and colour histogram with K-nearest neighbour classifiers. Procedia Comput. Sci. 58, 740–747 (2015)
Pawara, P., Okafor, E., Surinta, O., Schomaker, L., & Wiering, M.: Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition. In: International Conference on Pattern Recognition Applications and Methods, vol. 2, pp. 479–486. SCITEPRESS, February 2017
Söderkvist, O.: Computer vision classification of leaves from Swedish trees (2001)
Pandian, J.A., Geetharamani, G., Annette, B.: 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), pp. 199–204. IEEE, December 2019
https://www.kaggle.com/arunpandianj/collective-augmented-plantleaf-disease-dataset
Lee, S.H., Chang, Y.L., Chan, C.S., Remagnino, P.: Plant Identification System based on a Convolutional Neural Network for the LifeClef 2016 Plant Classification Task. CLEF (Working Notes) 1, 502–510 (2016)
Dvornik, N., Mairal, J., Schmid, C.:. Modeling visual context is key to augmenting object detection datasets. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 364–380 (2018)
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1–8. IEEE, September 2009
Hsiao, J. K., Kang, L. W., Chang, C. L., Lin, C.Y.: Comparative study of leaf image recognition with a novel learning-based approach. In: 2014 Science and Information Conference, pp. 389–393. IEEE, August 2014
Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.: Leafsnap: a computer vision system for automatic plant species identification. In: European Conference on Computer Vision, pp. 502–516. Springer, Berlin, Heidelberg, October 2012
Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pp. 722–729. IEEE, December 2008
Wang, Z., Lu, B., Chi, Z., Feng, D.: Leaf image classification with shape context and sift descriptors. In: 2011 International Conference on Digital Image Computing: Techniques and Applications, pp. 650–654. IEEE, December 2011
Bama, B.S., Valli, S.M., Raju, S., Kumar, V.A.: Content based leaf image retrieval (CBLIR) using shape, color and texture features. Ind. J. Comput. Sci. Eng. 2(2), 202–211 (2011)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9
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Marzougui, F., Elleuch, M., Kherallah, M. (2021). Evaluation of Data Augmentation for Detection Plant Disease. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_47
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