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All issues Volume 216 (2025) SHS Web Conf., 216 (2025) 01076 References
Open Access
Issue
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
Article Number 01076
Number of page(s) 7
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601076
Published online 23 May 2025
  1. S.P. Mohanty, D.P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016) [CrossRef] [Google Scholar]
  2. K.P. Ferentinos, Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018) [CrossRef] [Google Scholar]
  3. M. Jena, R.K. Behera, S. Dehuri, Hybrid decision tree for machine learning: A big data perspective, in Advances in machine learning for big data analysis (Springer Nature Singapore, Singapore, 2022), pp. 223–239 [CrossRef] [Google Scholar]
  4. E.C. Too, L. Yujian, S. Njuki, L. Yingchun, A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019) [CrossRef] [Google Scholar]
  5. A. Al Mamun, D. Ahmedt-Aristizabal, M. Zhang, M.I. Hossen, Z. Hayder, M. Awrangjeb, Plant Disease Detection Using Self-supervised Learning: A Systematic Review. IEEE Access (2024) [Google Scholar]
  6. K. Veerasamy, E.T. Fredrik, Intelligent Farming based on Uncertainty Expert System with Butterfly Optimization Algorithm for Crop Recommendation. J. Internet Serv. Inf. Secur. 13(4), 158–169 (2023) [Google Scholar]
  7. J. Amara, B. Bouaziz, A. Algergawy, A deep learning-based approach for banana leaf disease classification. Comput. Electron. Agric. 159, 95–104 (2017) [Google Scholar]
  8. Z. Zhang, T. Liu, J. Gao, M. Yang, W. Luo, F. Lin, TDR-Model: Tomato Disease Recognition Based on Image Dehazing and Improved MobileNetV3 Model. IEEE Access (2024) [Google Scholar]
  9. A. Radhika, M.S. Masood, Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique. J. Internet Serv. Inf. Secur. 12(4), 177–196 (2022) [Google Scholar]
  10. M.G. Selvaraj, A. Vergara, H. Ruiz, et al., AI-powered banana diseases and pest detection. Front. Plant Sci. 11, 41 (2020) [CrossRef] [Google Scholar]
  11. B. Liu, Y. Zhang, D. He, Y. Li, Identification of apple leaf diseases based on deep convolutional neural networks. Plant Methods 13, 49 (2017) [CrossRef] [Google Scholar]
  12. P. Angin, M.H. Anisi, F. Göksel, C. Gürsoy, A. Büyükgülcü, Agrilora: a digital twin framework for smart agriculture. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 11(4), 77–96 (2020) [Google Scholar]
  13. V. Singh, A.K. Misra, Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017) [Google Scholar]
  14. M. Brahimi, K. Boukhalfa, A. Moussaoui, Deep learning for plant diseases: Detection and saliency map visualization, in Human and Machine Learning (2018), pp. 50–58 [Google Scholar]
  15. Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat. Eng. Sci. 8(3), 214–232 (2023) [Google Scholar]
  16. K.P. Ferentinos, Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018) [CrossRef] [Google Scholar]
  17. M.I. Abdullah, S.K. Hao, I. Abdullah, S. Faizah, Parkinson's Disease Symptom Detection using Hybrid Feature Extraction and Classification Model, in Proceedings of the IEEE 14th Control and System Graduate Research Colloquium (ICSGRC 2023), pp. 93–98 (2023) [CrossRef] [Google Scholar]
  18. K. Veerasamy, E.J. Thomson Fredrik, Intelligence System towards Identifying Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 14(4), 45–59 (2023) [Google Scholar]
  19. S.S. Ahmad, M.I. Habelamateen, Application of Artificial Intelligence and Machine Learning in Software Defined Networks. J. Smart Internet Things (2023) [Google Scholar]
  20. R. Udayakumar, M. Anuradha, Y.M. Gajmal, R. Elankavi, Anomaly detection for internet of things security attacks based on recent optimal federated deep learning model. J. Internet Serv. Inf. Secur. 13(3), 104–121 (2023) [Google Scholar]

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