Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network
Keywords:
convolutional neural network, differential amplification, wheat leaf diseases, image identificationAbstract
In this study, a differential amplification convolutional neural network (DACNN) was proposed and used in the identification of wheat leaf disease images with ideal accuracy. The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output, which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability. Firstly, since there is no large-scale wheat leaf disease images dataset at present, the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images, and five kinds of data augmentation methods were used to expand the dataset. Secondly, DACNN combined four classifiers: Softmax, support vector machine (SVM), K-nearest neighbor (KNN) and Random Forest to evaluate the wheat leaf disease dataset. Finally, the DACNN was compared with the models: LeNet-5, AlexNet, ZFNet and Inception V3. The extensive results demonstrate that DACNN is better than other models. The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%. Keywords: convolutional neural network, differential amplification, wheat leaf diseases, image identification DOI: 10.25165/j.ijabe.20201304.4826 Citation: Dong M P, Mu S M, Shi A J, Mu W Q, Sun W J. Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network. Int J Agric & Biol Eng, 2020; 13(4): 205–210.References
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[6] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016; pp.2818–2826.
[7] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016; pp.770–778.
[8] Huang G, Liu Z, Laurens V D M, Weinberger K Q. Densely connected convolutional networks. Computer Vision and Pattern Recognition, IEEE, 2017; pp.4700-4708. doi: 10.1109/CVPR.2017.243.
[9] Khan A, Sohail A, Ali A. A new channel boosted convolutional neural network using transfer learning. arXiv preprint, 2018. arXiv:1804.08528.
[10] Hou S H, Wang Z L. Weighted channel dropout for regularization of deep convolutional neural network. AAAI Conference on Artificial Intelligence, 2019; 33: 8425–8432.
[11] Zeng W H, Li M, Li Z, Xiong Y. High-order residual and parameter-sharing feedback convolutional neural network for crop disease recognition. Acta Electronica Sinica, 2019; 47(9): 1979–1986.
[12] Zhang K, Guo Y R, Wang X S, Yuan J S, Ding Q L. Multiple feature reweight Dense Net for image classification. IEEE Access, 2019; 7: 9872–9880.
[13] Amanda R, Kelsee B, Peter M, Babuali A, James L, David P. Deep learning for image-based cassava disease detection. Frontiers in Plant Science, 2017; 8: 1852. doi: 10.3389/fpls.2017.01852.
[14] Mohanty S P, Hughes D P, Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2016; 7: 1419. doi: 10.3389/fpls.2016.01419.
[15] Lu Y, Yi S J, Zeng N Y, Liu Y R, Zhang Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 2017; 267(Dec.6): 378–384.
[16] Zhang S W, Xie Z Q, Zhang Q Q. Application research on convolutional neural network for cucumber leaf disease recognition. Jiangsu Journal of Agricultural Sciences, 2018; 34(1): 56 – 61.
[17] Huang S P, Sun C, Qi L, Ma X, Wang W J. Rice panicle blast identification method based on deep convolution neural network. Transactions of the CSAE, 2017; 33(20): 169 – 176.
[18] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. Neural Information Processing Systems, 2017; pp.3856–3866.
[19] Deng F, Pu S L, Chen X H, Shi Y S, Yuan T, Pu S Y. Hyperspectral image classification with capsule network using limited training samples. Sensors, 2018; 18(9): 3153. doi: 10.3390/s18093153.
[20] Gan H M, Yue X J, Hong T S, Ling K J, Wang L H, Cen Z Z. A hyperspectral inversion model for predicting chlorophyll content of Longan leaves based on deep learning. Journal of South China Agricultural University, 2018; 39(3): 102–110.
[21] Zhu, X L, Zhu M, Ren H. Method of plant leaf recognition based on improved deep convolutional neural network. Cognitive Systems Research, 2018; 52(Dec.): 223–233.
[22] Srivastava N, Hinton G, Krizhevsky A Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014; 15: 1929–1958.
[23] Hu J L, Lu J W, Tan Y P, Zhou J. Deep transfer metric learning. IEEE Transactions on Image Processig, 2016; 25(12): 5576–5588.
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Published
2020-08-07
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Dong, M., Mu, S., Shi, A., Mu, W., & Sun, W. (2020). Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network. International Journal of Agricultural and Biological Engineering, 13(4), 205–210. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4826
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Information Technology, Sensors and Control Systems
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