EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 341 (2025) EPJ Web Conf., 341 (2025) 01053 References
Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01053
Number of page(s) 7
DOI https://doi.org/10.1051/epjconf/202534101053
Published online 20 November 2025
  1. FAO, The State of Food Security and Nutrition in the World 2022. Food and Agriculture Organization of the United Nations, 2022. [Google Scholar]
  2. R. R. Patil, S. Kumar, S. Chiwhane, R. Rani, and S. K. Pippal, "An Artificial-Intelligence-Based Novel Rice Grade Model for Severity Estimation of Rice Diseases," Agriculture, vol. 13, no. 1, p. 47, Dec. 2022, doi: 10.3390/agriculture13010047. [Google Scholar]
  3. M. S. A. M. Al-Gaashani et al., "Deep transfer learning with gravitational search algorithm for enhanced plant disease classification," Heliyon, vol. 10, p. e28967, Dec. 2023, doi: 10.1016/j.heliyon.2024.e28967. [Google Scholar]
  4. M. Bagga and S. Goyal, "Image-based detection and classification of plant diseases using deep learning: State-of-the-art review," Urban Agriculture & Regional Food Systems, vol. 9, no. 1, p. e20053, 2024, doi: 10.1002/uar2.20053. [Google Scholar]
  5. S. K. Noon, M. Amjad, M. A. Qureshi, and A. Mannan, "Handling Severity Levels of Multiple Co-Occurring Cotton Plant Diseases Using Improved YOLOX Model," IEEE Access, vol. 10, pp. 134811-134824, Dec. 2022, doi: 10.1109/ACCESS.2022.3232751. [Google Scholar]
  6. C. Madhurya and E. A. Jubilson, "YR2S: Efficient Deep Learning Technique for Detecting and Classifying Plant Leaf Diseases," IEEE Access, vol. 11, pp. 3343450, Dec. 2023, doi: 10.1109/ACCESS.2023.3343450. [Google Scholar]
  7. X. Guan et al., "A lightweight model for efficient identification of plant diseases and pests based on deep learning," Frontiers in Plant Science, vol. 14, p. 1227011, 2023, doi: 10.3389/fpls.2023.1227011. [Google Scholar]
  8. E. A. Aldakheel, M. Zakariah, and A. H. Alabdalall, "Detection and identification of plant leaf diseases using YOLOv4," Frontiers in Plant Science, vol. 15, p. 1355941, 2024, doi: 10.3389/fpls.2024.1355941. [Google Scholar]
  9. D. S. Joseph, P. M. Pawar, and K. Chakradeo, "Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning," IEEE Access, vol. 12, pp. 16310-16333, 2024, doi: 10.1109/ACCESS.2024.3358333. [CrossRef] [Google Scholar]
  10. T. Shi, Y. Liu, X. Zheng et al., "Recent advances in plant disease severity assessment using convolutional neural networks," Scientific Reports, vol. 13, p. 2336, 2023, doi: 10.1038/s41598-023-29230-7. [Google Scholar]
  11. M. H. Saad and A. E. Salman, "A plant disease classification using one-shot learning technique with field images," Multimedia Tools and Applications, vol. 83, pp. 58935-58960, 2024, doi: 10.1007/s11042-023-17830-4. [Google Scholar]
  12. M. Shoaib et al., "An advanced deep learning models-based plant disease detection: A review of recent research," Frontiers in Plant Science, vol. 14, p. 1158933, 2023, doi: 10.3389/fpls.2023.1158933. [Google Scholar]
  13. S. M. Javidan, A. Banakar, K. Rahnama, K. A. Vakilian, and Y. Ampatzidis, "Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review," Smart Agricultural Technology, vol. 8, Art. no. 100480, 2024, doi: 10.1016/j.atech.2024.100480. [Google Scholar]
  14. A. Pal and V. Kumar, "AgriDet: Plant Leaf Disease severity classification using agriculture detection framework," Engineering Applications of Artificial Intelligence, vol. 119, Art. no. 105754, 2023, doi: 10.1016/j.engappai.2022.105754. [Google Scholar]
  15. A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, "Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations," Frontiers in Plant Science, vol. 15, p. 1356260, 2024, doi: 10.3389/fpls.2024.1356260. [Google Scholar]
  16. S. Sagar, M. Javed, and D. S. Doermann, "Leaf-Based Plant Disease Detection and Explainable AI," arXiv preprint, arXiv:2404.16833v1 [cs.CV], Dec. 2023. [Google Scholar]
  17. L. M. Abou El-Maged, A. Darwish, and A.E. Hassanien, "Artificial Intelligence-Based Plant Diseases Classification," in Machine Learning and Big Data Analytics Paradigms, A.E. Hassanien and A. Darwish, Eds., Studies in Big Data, vol. 77, Springer, Cham, 2021, doi: 10.1007/978-3-030-59338-43. [Google Scholar]
  18. D. Faye, I. Diop, N. Mbaye, D. Dione, and M. Diedhiou, "Plant Disease Severity Assessment Based on Machine Learning and Deep Learning: A Survey," Journal of Computer and Communications, vol. 11, pp. 57-75, 2023, doi: 10.4236/jcc.2023.119004. [Google Scholar]
  19. W. B. Demilie, "Plant disease detection and classification techniques : a comparative study of the performances," Journal of Big Data, vol. 11, p. 5, 2024, doi: 10.1186/s40537-023-00863-9. [Google Scholar]
  20. J. Yao, S. N. Tran, S. Sawyer et al., "Machine learning for leaf disease classification: data, techniques and applications," Artificial Intelligence Review, vol. 56 (Suppl 3), pp. 3571-3616, 2023, doi: 10.1007/s10462-023-10610-4. [Google Scholar]
  21. I. Yag and A. Altan, "Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments," Biology, vol. 11, no. 12, p. 1732, 2022, doi: 10.3390/biology11121732. [Google Scholar]
  22. U. A. Bhatti et al., "Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data," Computational Materials & Continua, vol. 77, no. 1, pp. 681-697, 2023, doi: 10.32604/cmc.2023.037958. [Google Scholar]
  23. R. U. Khan, K. Khan, W. Albattah, and A. M. Qamar, "Image-Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey," Wireless Communications and Mobile Computing, vol. 2021, Art. ID 5541859, pp. 1-13, 2021, doi: 10.1155/2021/5541859. [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.