The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Nanotechnology and artificial intelligence to enable sustainable and precision agriculture
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Planetary Boundaries: Exploring the Safe Operating Space for Humanity
Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S. I., Lambin, E., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., et al. (2009) · Ecology and Society · doi:10.5751/es-03180-140232 · Cited by 7,187
OERKE, E.-C. (2006) · The Journal of Agricultural Science · doi:10.1017/s0021859605005708 · Cited by 5,283
Impact of pesticides use in agriculture: their benefits and hazards
Aktar, W., Sengupta, D., & Chowdhury, A. (2009) · Interdisciplinary Toxicology · doi:10.2478/v10102-009-0001-7 · Cited by 3,584
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294
Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.05.012 · Cited by 1,577
Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J., & Garnier, J. (2014) · Environmental Research Letters · doi:10.1088/1748-9326/9/10/105011 · Cited by 1,219
Plant nanobionics approach to augment photosynthesis and biochemical sensing
Giraldo, J. P., Landry, M. P., Faltermeier, S. M., McNicholas, T. P., Iverson, N. M., Boghossian, A. A., Reuel, N. F., Hilmer, A. J., Sen, F., Brew, J. A., et al. (2014) · Nature Materials · doi:10.1038/nmat3890 · Cited by 1,158
Pesticide residues in European agricultural soils – a hidden reality unfolded
Silva, V., Mol, H. G. J., Zomer, P., Tienstra, M., Ritsema, C. J., & Geissen, V. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.441 · Cited by 1,153
Greenhouse gas mitigation by agricultural intensification
Burney, J. A., Davis, S. J., & Lobell, D. B. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914216107 · Cited by 1,128
Cited by
Technologies and perspectives for achieving carbon neutrality
Wang, F., Harindintwali, J. D., Yuan, Z., Wang, M., Wang, F., Li, S., Yin, Z., Huang, L., Fu, Y., Li, L., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100180 · Cited by 1,330
Mana, A. A., Allouhi, A., Hamrani, A., Rehman, S., el Jamaoui, I., & Jayachandran, K. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100416 · Cited by 245
Ravi, N., Chaturvedi, P., Huerta, E. A., Liu, Z., Chard, R., Scourtas, A., Schmidt, K. J., Chard, K., Blaiszik, B., & Foster, I. (2022) · Scientific Data · doi:10.1038/s41597-022-01712-9 · Cited by 51
Metadata
- Authors
- Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I.
- Venue
- Nature Plants
- Published
- 2021-06-24
- Volume
- 7
- Issue
- 7
- Pages
- 864-876
- ISSN
- 2055-026X (ISSN-L)
- 2055-0278
- OpenAlex
- W3176680278
- Cited by
- 478
- References
- 96
- FWCI
- 18.41
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021). Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants. https://doi.org/10.1038/s41477-021-00946-6
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
