Open Access
Issue
E3S Web Conf.
Volume 477, 2024
International Conference on Smart Technologies and Applied Research (STAR'2023)
Article Number 00081
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202447700081
Published online 16 January 2024
  1. Qazi, S., Khawaja, B. A., & Farooq, Q. U. (2022). IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends. IEEE Access, 10, 21219-21235. [CrossRef] [Google Scholar]
  2. Charania, I., & Li, X. (2020). Smart farming: Agriculture's shift from a labor intensive to technology native industry. Internet of Things, 9, 100142. [CrossRef] [Google Scholar]
  3. Puri, V., Nayyar, A., & Raja, L. (2017). Agriculture drones: A modern breakthrough in precision agriculture. Journal of Statistics and Management Systems, 20(4), 507-518. [CrossRef] [Google Scholar]
  4. García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020). IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors, 20(4), 1042. [CrossRef] [PubMed] [Google Scholar]
  5. Fu, C., Xu, C., Xue, M., Liu, W., & Yang, S. (2021). Data-driven decision making based on evidential reasoning approach and machine learning algorithms. Applied Soft Computing, 110, 107622. [CrossRef] [Google Scholar]
  6. Slimani, K., Ruichek, Y., & Messoussi, R. (2022). Compound facial emotional expression recognition using cnn deep features. Engineering Letters, 30(4). [Google Scholar]
  7. Sreekantha, D. K., & Kavya, A. M. (2017, January). Agricultural crop monitoring using IOT-a study. In 2017 11th International conference on intelligent systems and control (ISCO) (pp. 134-139). IEEE. [Google Scholar]
  8. Ashraf, H., & Akanbi, M. T. (2023). Sustainable Agriculture in the Digital Age: Crop Management and Yield Forecasting with IoT, Cloud, and AI. Tensorgate Journal of Sustainable Technology and Infrastructure for Developing Countries, 6(1), 64-71. [Google Scholar]
  9. Tomaszewski, L., Kołakowski, R., & Zagórda, M. (2022, June). Application of mobile networks (5G and beyond) in precision agriculture. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 71-86). Cham: Springer International Publishing. [Google Scholar]
  10. Slimani, K., Khoulji, S., & Kerkeb, M. L. (2023). Advancements and challenges in energy-efficient 6G mobile communication network. In E3S Web of Conferences (Vol. 412, p. 01036). EDP Sciences. [Google Scholar]
  11. Shaikh, T. A., Rasool, T., & Lone, F. R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. [CrossRef] [Google Scholar]
  12. Mohamed, E. S., Belal, A. A., Abd-Elmabod, S. K., El-Shirbeny, M. A., Gad, A., & Zahran, M. B. (2021). Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science, 24(3), 971-981. [CrossRef] [Google Scholar]
  13. Junaid, M., Shaikh, A., Hassan, M. U., Alghamdi, A., Rajab, K., Al Reshan, M. S., & Alkinani, M. (2021). Smart agriculture cloud using AI based techniques. Energies, 14(16), 5129. [CrossRef] [Google Scholar]
  14. Parasuraman, K., Anandan, U., & Anbarasan, A. (2021, February). IoT based smart agriculture automation in artificial intelligence. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 420-427). IEEE. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.