Open Access
Issue |
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
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Article Number | 01063 | |
Number of page(s) | 6 | |
Section | Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development | |
DOI | https://doi.org/10.1051/shsconf/202521601063 | |
Published online | 23 May 2025 |
- O. I. Al-Sanjary, S. Vasuthevan, H. K. Omer, M. N. Mohammed, M. I. Abdullah, An Intelligent Recycling Bin Using Wireless Sensor Network Technology, in Proceedings of the 2019 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2019, 30–33 (2019) [Google Scholar]
- A. Gupta, A. K. Srivastava, Artificial Intelligence - Smart Energy Distribution and Management System for small autonomous Photo-voltaic System, in Proceedings of the 2023 1st International Conference on Intelligent Computing and Research Trends (ICRT), IEEE Sponsored Conference, Roorkee Institute of Technology, Roorkee, February 3-4 (2023) [Google Scholar]
- S. Bandopadhyay, S. Dey, L. Grover, S. Ghosh, B. Das, Sensing the dynamic nature of fragmented croplands in India through earth observation: a comprehensive review. EarthArXiv (to be published) [Google Scholar]
- A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, H. Adam, MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). https://doi.org/10.48550/arXiv.1704.04861 [Google Scholar]
- K. Veerasamy, E. J. T. Fredrik, Intelligence System towards Identify Weeds in Crops and Vegetables Plantation Using Image Processing and Deep Learning Techniques. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 14, 45–59 (2023) [Google Scholar]
- F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv preprint arXiv:1602.07360 (2016) [Google Scholar]
- A. Kamilaris, F. X. Prenafeta-Boldú, Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018) [CrossRef] [Google Scholar]
- A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in neural information processing systems, 1097–1105 (2012) [Google Scholar]
- T. Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollár, Focal loss for dense object detection, in Proceedings of the IEEE international conference on computer vision, 2980–2988 (2017) [Google Scholar]
- W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, SSD: Single shot multibox detector, in European Conference on Computer Vision, 21–37 (Springer, Cham, 2016) [Google Scholar]
- Y. Camgözlü, Y. Kutlu, Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Nat. Eng. Sci. 8, 214–232 (2023) [Google Scholar]
- D. Moshou, C. Bravo, J. West, A. McCartney, H. Ramon, Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 44, 173–188 (2014) [Google Scholar]
- J. Redmon, A. Farhadi, YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018) [Google Scholar]
- S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, in Advances in neural information processing systems, 91–99 (2015) [Google Scholar]
- C. Shorten, T. M. Khoshgoftaar, A survey on image data augmentation for deep learning. J. Big Data 6, 1–48 (2019) [CrossRef] [Google Scholar]
- K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014) [Google Scholar]
- P. S. Sundari, M. Subaji, J. Karthikeyan, A survey on effective similarity search models and techniques for big data processing in healthcare system. Res. J. Pharm. Technol. 10, 2677–2684 (2017) [CrossRef] [Google Scholar]
- D. Tilman, C. Balzer, J. Hill, B. L. Befort, Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. 108, 20260–20264 (2011) [CrossRef] [PubMed] [Google Scholar]
- M. Subramanian, K. Shanmugavadivel, P. S. Nandhini, On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Comput. Appl. 34, 13951–13968 (2022) [CrossRef] [Google Scholar]
- I. Arel, D. C. Rose, T. P. Karnowski, Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 5, 13–18 (2010) [CrossRef] [Google Scholar]
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