Open Access
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
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.