Automation of Agriculture Based on Deep Learning: Modeling and Management to Improve Quality and Efficiency-Reference-Cited by-同舟云学术

Automation of Agriculture Based on Deep Learning: Modeling and Management to Improve Quality and Efficiency

Published:2023 Issue: Volume: Page:131-137
ISSN:
Container-title:Food Security in the Economy of the Future
language:
Short-container-title:

Author:

Przhedetskaya Natalia V.^ORCID,Nagovitsyna Eleonora V.^ORCID,Przhedetskaya Victoria Yu.^ORCID,Borzenko Ksenia V.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-23511-5_14

Reference12 articles.

1. Presidential Executive Office. (2022). Decree “On approval of the Food Security Doctrine of the Russian Federation” (January 21, 2020, No. 20). http://www.kremlin.ru/acts/bank/45106. Accessed 9 May 2022.

2. Ministry of Agriculture of the Russian Federation. (2022). Departmental project “Digital agriculture.” https://mcx.gov.ru/upload/iblock/900/900863fae06c026826a9ee43e124d058.pdf. Accessed 9 May 2022.

3. Khan, S., Tufail, M., Khan, M. T., Khan, Z. A., & Anwar, S. (2021). Deep learning-based identification system of weeds and crops in strawberry and pea fields for a precision agriculture sprayer. Precision Agriculture, 22, 1711–1727. https://doi.org/10.1007/s11119-021-09808-9

4. Rao, I., Shirgire, P., Sanganwar, S., Vyawhare, K., & Vispute, S. R. (2022). An overview of agriculture data analysis using machine learning techniques and deep learning. In J. I.-Z. Chen, J. M. R. S. Tavares, A. M. Iliyasu, & K L. Du (Eds.), Second international conference on image processing and capsule networks (pp. 343–355). Springer. https://doi.org/10.1007/978-3-030-84760-9_30

5. Saleem, M. H., Potgieter, J., & Arif, K. M. (2021). Automation in agriculture by machine and deep learning techniques: A review of recent developments. Precision Agriculture, 22, 2053–2091. https://doi.org/10.1007/s11119-021-09806-x