Open Access
| Issue |
BIO Web Conf.
Volume 194, 2025
International Scientific Conference on Biotechnology and Food Technology (BFT-2025)
|
|
|---|---|---|
| Article Number | 01064 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/bioconf/202519401064 | |
| Published online | 14 November 2025 | |
- A. Nicolas, J.-L. Barrat, J. Rottler, Effects of inertia on the steady-shear rheology of disordered solids. Phys. Rev. Lett. 116, 058303 (2016). https://doi.org/10.1103/PhysRevLett.116.058303 [CrossRef] [PubMed] [Google Scholar]
- A. Lohrasebi, T. Koslowski, Modeling water purification by an aquaporin-inspired graphene-based nano-channel. J. Mol. Model. 25, 280 (2019). https://doi.org/10.1007/s00894-019-4160-y [CrossRef] [PubMed] [Google Scholar]
- T. Wang, et.al., Applications of machine vision in agricultural robot navigation: A review. Comput. Electron. Agric. 198, 107085 (2022). https://doi.org/10.1016/j.compag.2022.107085 [Google Scholar]
- H. Kamilaris, F.X. Prenafeta-Boldú, Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018). https://doi.org/10.1016/j.compag.2018.02.016 [CrossRef] [Google Scholar]
- J. Schmidhuber, Deep learning in neural networks: An overview. Neural Networks 61, 85–117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003 [CrossRef] [Google Scholar]
- Y.Song, et.al., Assessment of wheat chlorophyll content by the multiple linear regression of leaf image features. Inf. Process. Agric. 8(2), 232–243 (2021). https://doi.org/10.1016/j.inpa.2020.05.002 [Google Scholar]
- R. Khaki, L. Wang, Crop yield prediction using deep neural networks. Front. Plant Sci. 10, 621 (2019). https://doi.org/10.3389/fpls.2019.00621 [Google Scholar]
- K. Liakos, P. Busato, D. Moshou, S. Pearson, D. Bochtis, Machine learning in agriculture: A review. Sensors 18, 2674 (2018). https://doi.org/10.3390/s18082674 [CrossRef] [Google Scholar]
- Y.Yu, et.al., Mechanisms underlying nitrous oxide emissions and nitrogen leaching from potato fields under drip irrigation and furrow irrigation. Agric. Water Manag. 260, 107270 (2022). https://doi.org/10.1016/j.agwat.2021.107270 [Google Scholar]
- J.M.R. Gorle, et.al., Hydrodynamics of octagonal culture tanks with Cornell-type dual-drain system. Comput. Electron. Agric. 151, 354–364 (2018). https://doi.org/10.1016/j.compag.2018.06.012 [Google Scholar]
- FAO, The State of Food and Agriculture 2021. FAO, Rome (2021). https://doi.org/10.4060/cb4476en [Google Scholar]
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