Open Access
| Issue |
BIO Web Conf.
Volume 113, 2024
XVII International Scientific and Practical Conference “State and Development Prospects of Agribusiness” (INTERAGROMASH 2024)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 8 | |
| Section | Plant Production | |
| DOI | https://doi.org/10.1051/bioconf/202411301014 | |
| Published online | 18 June 2024 | |
- Dutot M., Nelson L., Tyson, R. (2013). Postharvest Biol. Technol. 85, 45–56. doi: 10.1016/j.postharvbio.2013.04.003 [CrossRef] [Google Scholar]
- Gu J., Wang Z., Kuen J., Ma L., Shahroudy A., Bing Sh., Liu, T., Wang, X., Gang, W. 2017. Recent Advances in Convolutional Neural Networks. arXiv preprint arXiv:1512.07108 [Google Scholar]
- Fuentes A., Yoon S., Kim S.C., and Park D.S. (2017). Sensors 17:2022. doi: 10.3390/s17092022 [CrossRef] [Google Scholar]
- Ramcharan A., Baranowski K., McCloskey P., Ahmed B., and Legg, J. (2017). Front. Plant Sci. 8:1852. doi: 10.3389/fpls.2017.01852 [CrossRef] [Google Scholar]
- Zhu Y., Sun W., Cao X., Wang C., Wu, D. (2019). Neurocomputing 365, 191–200. doi: 10.1016/j.neucom.2019.07.016 [CrossRef] [Google Scholar]
- Saleem, M.H., Potgieter, J., and Mahmood Arif, K. (2019). Plants 8:468. doi: 10.3390/plants8110468 [CrossRef] [PubMed] [Google Scholar]
- Waghmare H., Kokare R., Dandawate, Y. (2016). Detection and classification of diseases of Grape plant using opposite colour Local Binary Pattern feature and machine learning for automated Decision Support System,” (International Conference on Signal Processing and Integrated Networks), 513–518. doi: 10.1109/SPIN.2016.7566749 [Google Scholar]
- Mokashi M., Uzma Afreen Bagayat, Areeba, Q., Muskan, N. (2022). Journal of Healthcare Engineering 9(8): 73–78. doi:10.1729/Journal.31332 [Google Scholar]
- Mohammadpoor M., Nooghabi M.G., Ahmedi, Z. (2020). Int. J. Interact. Multimedia Artif. Intell. 6, 62–67. doi: 10.9781/ijimai.2020.02.001 [Google Scholar]
- A.T. Rybak, R. Gorbunov, A.V. Olshevskaya [et al.] Precision Agriculture and Agricultural Machinery Industry. Volume 1, Verlag, 2022. - P. 207–216. - DOI 10.1007/978-3-030-81619-3_23. [Google Scholar]
- A method of yield monitoring based on neural networks using deep learning / N. Gapon, A. Azhinov, M. Zhdanova [et al.] // E3s web of conferences : International Scientific Conference “Fundamental and Applied Scientific Research in the Development of Agriculture in the Far East” (AFE-2023). Vol. 462. - EDP Sciences: EDP Sciences, 2023. - P. 02016. - DOI 10.1051/e3sconf/202346202016. [Google Scholar]
- Li G., Ma Z., Wang, H. Image recognition of grape downymildew and grape powdery mildew based on support vectormachine. In: Proc international conference on computer andcomputing technologies in agriculture. Beijing, China. p. 151–162. [Google Scholar]
- Kulin M., Kazaz, T., Moerman, I., Poorter, E.D. (2017). End-to-end Learning from Spectrum proach for Wireless Signal Identification in Spectrum Monitoring applications. IEEE Access 6, 18484–18501. doi: 10.1109/ACCESS.2018.2818794 [CrossRef] [Google Scholar]
- Wagh T.A., Samant R.M., Gujarathi S.V., Gaikwad, S.B. (2019). Int. J. Comput. Appl. 178, 7–11. doi: 10.5120/ijca2019918982 [Google Scholar]
- Ji M., Zhang, L., Wu, Q. (2019). Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks. Inf. Process. Agric. 1-9. doi: 10.1016/j.inpa.2019.10.003. [Google Scholar]
- Liu B., Ding Z., Tian L., He D., Li, S. and Wang, H. (2020). Front. Plant Sci. 11:1082. doi: 10.3389/fpls.2020.01082. [CrossRef] [Google Scholar]
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