Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network
Abstract
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.
Keywords
Full Text:
PDFReferences
Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc.IEEE, 1998; 86(11): 2278–2324.
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. Proceedings of Advances in Neural Information Processing Systems, 2012; 25: 1097−1105.
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. Computer Vision-ECCV, IEEE, 2014; 8689: 818–833.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint, 2014; 6: 1–47. arXiv 1409.1556.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston: IEEE, 2015; pp.1–9.
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.
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.
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.
Khan A, Sohail A, Ali A. A new channel boosted convolutional neural network using transfer learning. arXiv preprint, 2018. arXiv:1804.08528.
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.
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.
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.
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.
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.
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.
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.
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.
Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. Neural Information Processing Systems, 2017; pp.3856–3866.
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.
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.
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.
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.
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.
Copyright (c) 2020 International Journal of Agricultural and Biological Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.