Issue |
BIO Web Conf.
Volume 71, 2023
II International Conference on Current Issues of Breeding, Technology and Processing of Agricultural Crops, and Environment (CIBTA-II-2023)
|
|
---|---|---|
Article Number | 01117 | |
Number of page(s) | 7 | |
Section | Issues of Sustainable Development of Agriculture | |
DOI | https://doi.org/10.1051/bioconf/20237101117 | |
Published online | 07 November 2023 |
- A. Mecke, I. Lee, J.R. Baker jr., M.M. Banaszak Holl, B.G. Orr, Eur. Phys. J. E 14, 7 (2004) [CrossRef] [Google Scholar]
- M. Ben Rabha, M.F. Boujmil, M. Saadoun, B. Bessaïs, Eur. Phys. J. Appl. Phys. (to be published) [Google Scholar]
- F. De Lillo, F. Cecconi, G. Lacorata, A. Vulpiani, EPL, 84 (2008) [Google Scholar]
- L. T. De Luca, Propulsion physics (EDP Sciences, Les Ulis, 2009) [Google Scholar]
- G. Plancque, D. You, E. Blanchard, V. Mertens, C. Lamouroux, Role of chemistry in the phenomena occurring in nuclear power plants circuits, in Proceedings of the International Congress on Advances in Nuclear power Plants, ICAPP, 2-5 May 2011, Nice, France (2011) [Google Scholar]
- D.P. Kingma, B.J. Adam, A Method for Stochastic Optimization, arXiv:1412.6980 (2014). [Google Scholar]
- K.P. Ferentinos, Comput. Electron. Agric. 145, 311–318 (2018). [CrossRef] [Google Scholar]
- E.C. Too, L. Yujian, S. Njuki, L. Yingchun, Comput. Electron. Agric. 161, 272–279 (2018). [Google Scholar]
- S. Savary, A. Ficke, Jean-Noël Aubertot, et al., Crop losses due to diseases and their implications for global food production losses and food security. Springer Food Security, 4, pp. 519–537 (2012). [CrossRef] [Google Scholar]
- M. Brahimi, M. Arsenovic, S. Laraba, S. Sladojevic, K. Boukhalfa, A. Moussaoui, Deep learning for plant diseases: detection and saliency map visualisation, in Human and Machine Learning (eds J. Zhou and F. Chen, Cham: Springer International Publishing, 2018) 93–117. [Google Scholar]
- H. Kaiming, Z. Xiangyu, R. Shaoqing, S. Jian, Deep Residual Learning for Image Recognition, https://arxiv.org/abs/1512.03385. [Google Scholar]
- TAN, Mingxing et LE, Quoc. Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning, 6105-6114 (2019). [Google Scholar]
- Sh.P. Mohanty, D.P. Hughes, M. Salathé, Frontiers in Plant Science 7, 1419 (2016). [CrossRef] [PubMed] [Google Scholar]
- Google Cloud Vision project Retrieved from: https://cloud.google.com/vision [Google Scholar]
- IBM Watson Visual Recognition project Retrieved from: https://www.ibm.com/watson/services/visual-recognition [Google Scholar]
- Microsoft Custom Vision project Retrieved from: https://www.customvision.ai [Google Scholar]
- Plant Disease Expert. Image Data set for Plant Disease detection Retrieved from: https://www.kaggle.com/datasets/sadmansakibmahi/plant-disease-expert [Google Scholar]
- E.S. Bogodukhova, E.O. Bobrova, et al., Directions for the development of renewable energy sources in Russia using information technologies during the formation of the climate crisis. IOP Conference Series: Earth and Environmental Science, 723(5) (2021). [Google Scholar]
- E.O. Bobrova, A.S. Zueva, et al., Trends in the formation of an alternative energy balance in Russia using information technologies during the climate crisis. IOP Conference Series: Earth and Environmental Science, 808(1) (2021). [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.