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 | 01060 | |
Number of page(s) | 7 | |
Section | Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development | |
DOI | https://doi.org/10.1051/shsconf/202521601060 | |
Published online | 23 May 2025 |
- S. Wolfert, G. Isakhanyan, Sustainable agriculture by the Internet of Things-A practitioner's approach to monitor sustainability progress. Comput. Electron. Agric. 200, 107226 (2022). https://doi.org/10.1016/j.compag.2022.107226 [CrossRef] [Google Scholar]
- A. Balafoutis, B. Beck, S. Fountas, J. Vangeyte, T. Wal, I. Soto, … Z. Tsiropoulos, Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability 9(8), 1339 (2017). https://doi.org/10.3390/su9081339 [CrossRef] [Google Scholar]
- J.G.A. Barbedo, Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2(1), 660 (2013). https://doi.org/10.1186/2193-1801-2-660 [CrossRef] [Google Scholar]
- L. Alamer, I.M. Alqahtani, E. Shadadi, Intelligent Health Risk and Disease Prediction Using Optimized Naive Bayes Classifier. J. Internet Serv. Inf. Secur. 13(1), 01–10 (2023) [Google Scholar]
- S. Sharma, C. Sharma, E. Asenso, K. Sharma, Research constituents and trends in smart farming: an analytical retrospection from the lens of text mining. J. Sensors 2023(1), 6916213 (2023). https://doi.org/10.1155/2023/6916213 [CrossRef] [Google Scholar]
- A. Kamilaris, F.X. Prenafeta-Boldú, Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70–90 (2018). https://doi.org/10.1016/j.compag.2017.09.037 [CrossRef] [Google Scholar]
- M. Rao, S. Kumar, K. Rao, Effective medical leaf identification using hybridization of GMMCNN. Int. J. Exp. Res. Rev. 32, 115–123 (2023) [CrossRef] [Google Scholar]
- K.G. Liakos, P. Busato, D. Moshou, S. Pearson, D. Bochtis, Machine learning in agriculture: A review. Sensors 18(8), 2674 (2018). https://doi.org/10.3390/s18082674 [CrossRef] [Google Scholar]
- A.K. Mahlein, Plant disease detection by imaging sensors-parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis. 100(2), 241–251 (2016). https://doi.org/10.1094/pdis-03-15-0340-fe [CrossRef] [PubMed] [Google Scholar]
- M.N. Mohammed, S. Al-Zubaidi, S.H. Kamarul Bahrain, M. Zaenudin, M.I. Abdullah, Design and Development of River Cleaning Robot Using IoT Technology, in Proceedings - 2020 16th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2020, 84–87 (2020) [CrossRef] [Google Scholar]
- S.P. Mohanty, D.P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016). https://doi.org/10.3389/fpls.2016.01419 [CrossRef] [Google Scholar]
- P. Patil, S. Kale, A model for smart agriculture using IoT, in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 543–545 (2016) [Google Scholar]
- S.J. Pethybridge, S.C. Nelson, Leaf Doctor: A new portable application for quantifying plant disease severity. Plant Dis. 99(10), 1310–1316 (2015). https://doi.org/10.1094/pdis-03-15-0319-re [CrossRef] [PubMed] [Google Scholar]
- M. Cooper, C.D. Messina, Can we harness "enviromics" to accelerate crop improvement by integrating breeding and agronomy?. Front. Plant Sci. 12, 735143 (2021). https://doi.org/10.3389/fpls.2021.735143 [CrossRef] [Google Scholar]
- R.R. Shamshiri, F. Kalantari, K.C. Ting, K.R. Thorp, I.A. Hameed, C. Weltzien, … Z. Shad, Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. Int. J. Agric. Biol. Eng. 11(1), 1–22 (2018). http://doi.org/10.25165/j.ijabe.20181101.3210 [Google Scholar]
- A. Rahman, D. Kundu, T. Debnath, M. Rahman, M.J. Islam, Blockchain-based AI Methods for Managing Industrial IoT: Recent Developments, Integration Challenges and Opportunities. arXiv preprint arXiv:2405.12550 (2024) [Google Scholar]
- G. Sushanth, S. Sujatha, IOT based smart agriculture system, in 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 1–4 (2018) [Google Scholar]
- D.C. Tsouros, S. Bibi, P.G. Sarigiannidis, A review on UAV-based applications for precision agriculture. Information 10(11), 349 (2019). https://doi.org/10.3390/info10110349 [CrossRef] [Google Scholar]
- S. Wolfert, L. Ge, C. Verdouw, M.J. Bogaardt, Big data in smart farming-a review. Agric. Syst. 153, 69–80 (2017). https://doi.org/10.1016/j.agsy.2017.01.023 [CrossRef] [Google Scholar]
- R.P. Sharma, D. Ramesh, P. Pal, S. Tripathi, C. Kumar, IoT-enabled IEEE 802. 15.4 WSN monitoring infrastructure-driven fuzzy-logic-based crop pest prediction. IEEE Internet Things J. 9(4), 3037–3045 (2021). https://doi.org/10.1109/JIOT.2021.3091595 [Google Scholar]
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