Open Access
Issue
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
Article Number 00032
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202560100032
Published online 16 January 2025
  1. H. Kolakaluru, T. Vishal, M. P. Chandu, M. Harshini, T. Vignesh, V. V. P. Padyala, Crop Disease Identification using Convolutional Neural Network, in Inter. Conf. on Inventive Comp. Tech., pp. 366–369. IEEE, Nepal (2023). [Google Scholar]
  2. H. Slimani, J. E. Mhamdi, A. Jilbab, Deep Learning Structure for Real-time Crop Monitoring Based on Neural Architecture Search and UAV. Brazilian Archives of Biology and Technology, 67, e24231141 (2024). [CrossRef] [Google Scholar]
  3. A. Y. Krishna, S. T. Sri, V. Sravya, P. S. Praneetha, B. V. Vardhan, Disease Recognition of Crops using ResNet and MDFC-ResNet, in: Inter. Conf. on Sust. Computing and Data Communication Systems, pp. 738–744. IEEE, India (2023). [Google Scholar]
  4. J. Kotwal, R. Kashyap, S. Pathan, Agricultural plant diseases identification: From traditional approach to deep learning. Materials Today: Proc. 80, 344–356 (2023). [CrossRef] [Google Scholar]
  5. H. Slimani, J. El Mhamdi, A. Jilbab, Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study. Inter. J. of Elec. and Comp. Eng. 14(1), 878–890 (2024). [Google Scholar]
  6. P. Sharma, R. K. Yadav, S. S. Rawat, Hybrid Models for Plant Disease Detection using Transfer Learning Technique, in 10th International Conference on Computing for Sustainable Global Development, pp. 712–718. IEEE, India (2023). [Google Scholar]
  7. H. Slimani, J. El Mhamdi, A. Jilbab, Drone-Assisted Plant Disease Identification Using Artificial Intelligence: A Critical Review. Inter. J. of Comp. and Dig. Sys. 14(1), 10433–10446 (2023). [CrossRef] [Google Scholar]
  8. H. Singh, U. Tewari, S. Ushasukhanya, Tomato Crop Disease Classification using Convolution Neural Network and Transfer Learning, in International Conference on Networking and Communications, pp. 1–6. IEEE, India (2023). [Google Scholar]
  9. H. Slimani, J. El Mhamdi, A. Jilbab, Artificial intelligence-based detection of fava bean rust disease in agricultural settings: an innovative approach. Inter. J. of Adv. Comp. Sci. and App. 14(6), 119–128 (2023). [Google Scholar]
  10. S. Tyagi, S. R. N. Reddy, R. Anand, A. Sabharwal, Enhancing rice crop health: a light weighted CNN-based disease detection system with mobile application integration. Multimedia Tools and Applications, 83(16), 48799–48829 (2024). [Google Scholar]
  11. G. Ouma, F. M. Awuor, C. R. Makiya, P. Okanda, A Framework for Enhancing Adoption of Mobile-based Surveillance for Crop Pest and Disease Management by Farmers in Kenya. Journal of Agricultural Informatics, 15(1) (2024). [CrossRef] [Google Scholar]

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