Open Access
Issue
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
Article Number 03027
Number of page(s) 6
Section Computing Methods and Computer Application
DOI https://doi.org/10.1051/matecconf/202235503027
Published online 12 January 2022
  1. Patrício D and Rieder R. 2018. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Comput. Electron. Agric. 153: 69-81. doi:10.1016/j.compag.2018.08.001. [CrossRef] [Google Scholar]
  2. Ni C, Wang D Y, Vinson R, Holmes M and Tao Y. 2019. Automatic inspection machine for maize kernels based on deep convolutional neural networks. Biosyst. Eng. 178: 131-144. doi:10.1016/j.biosystemseng.2018.11.010. [CrossRef] [Google Scholar]
  3. Kussul N, Lavreniuk M, Skakun S and Shelestov A. 2017. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters PP: 1-5. doi:10.1109/LGRS.2017.2681128. [Google Scholar]
  4. Ioffe S and Szegedy C. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. [Google Scholar]
  5. Santurkar S, Tsipras D, Ilyas A and Madry A. 2018. How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)in arXiv: 1805.11604. [Google Scholar]
  6. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z and Ieee. 2016. Rethinking the Inception Architecture for Computer Vision. 2016 Ieee Conference on Computer Vision and Pattern Recognition. Ieee, New York. p. 2818-2826. [CrossRef] [Google Scholar]
  7. He K M, Zhang X Y, Ren S Q, Sun J and Ieee. 2016. Deep Residual Learning for Image Recognition. 2016 Ieee Conference on Computer Vision and Pattern Recognition. Ieee, New York. p. 770-778. [Google Scholar]
  8. Szegedy C, Ioffe S, Vanhoucke V and Alemi A. 2016. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. in arXiv:1602.07261. [Google Scholar]
  9. Chollet F. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. 30th Ieee Conference on Computer Vision and Pattern Recognition. Ieee, New York. p. 1800-1807. [Google Scholar]
  10. Huang G, Liu Z, van der Maaten L and Weinberger K. 2017. Densely Connected Convolutional Networks. in arXiv:1608.06993. [Google Scholar]

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