Food Security Challenges to Agriculture and a Promising Response to Them in AgroTech
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3555-8_19
Reference19 articles.
1. Astafeva, O. V., Astafyev, E. V., Khalikova, E. A., Leybert, T. B., & Osipova, I. A. (2020). XBRL reporting in the conditions of digital business transformation. Lecture Notes in Networks and Systems, 84, 373–381.
2. Deléglise, H., Interdonato, R., Bégué, A., Teisseire, M., & Roche, M. (2022). Food security prediction from heterogeneous data combining machine and deep learning methods. Expert Systems with Applications, 190, 116189. https://doi.org/10.1016/j.eswa.2021.116189
3. Erdoğan, M. (2022). Assessing farmers’ perception to Agriculture 4.0 technologies: A new interval-valued spherical fuzzy sets-based approach. International Journal of Intelligent Systems, 37(2), 1751–1801. https://doi.org/10.1002/int.22756
4. Ho, Y.-S. Comments on: Li, et al. (2021). Knowledge structure of technology licensing based on co-keywords network: A review and future directions. International Review of Economics & Finance, 66, 154–165. International Review of Economics and Finance, 75, 267–268. https://doi.org/10.1016/j.iref.2021.03.018
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