Open Access
Issue |
E3S Web of Conf.
Volume 537, 2024
International Scientific and Practical Conference “Sustainable Development of the Environment and Agriculture: Green and Environmental Technologies” (SDEA 2024)
|
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Article Number | 08010 | |
Number of page(s) | 7 | |
Section | Digital and Engineering Technologies as a Factor in the Intensive Development of Agriculture | |
DOI | https://doi.org/10.1051/e3sconf/202453708010 | |
Published online | 13 June 2024 |
- M.V. Lysenko, Yu.V. Lysenko, V.D. Mingalev, V.M. Sharapova, Technical potential of agricultural organizations and its optimization. Agrarian Bulletin of the Urals, 12(166), 15 (2017) [Google Scholar]
- F.J. Chang, K.W. Wang, A systematical water allocation scheme for drought mitigation. Journal of hydrology, 507, 124–133 (2014) [Google Scholar]
- S.I. Abba, Q.B. Pham, G. Saini, Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index. Environmental Science and Pollution Research, 7, 41524–41539 (2020) [Google Scholar]
- H. Afzaal, A.A. Farooque, F. Abbas, B. Acharya, T. Esau, Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management. Applied sciences-Basel., 10(5), 1621 (2020) [CrossRef] [Google Scholar]
- H. Chen, J.J. Huang, E. McBean, Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland. Agricultural water management, 228, 105923 (2020) [CrossRef] [Google Scholar]
- D. Freeman, Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress. Remote sensing, 11(22), 2645 (2019) [CrossRef] [Google Scholar]
- S. Mehdizadeh, J. Behmanesh, K. Khalili, Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and electronics in agriculture, 139, 103–114 (2017) [CrossRef] [Google Scholar]
- O. Rahmati, Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the total environment, 699(8), 134230 (2019) [Google Scholar]
- A. Sumarudin, Implementation irrigation system using Support Vector Machine for precision agriculture based on IoT. 5th Annual applied science and engineering conference (AASEC 2020). IOP Conference Series : Materials Science and Engineering, 1098, 032098 (2021) [CrossRef] [Google Scholar]
- V. Vijayakumar, N. Balakrishnan, Artificial intelligence-based agriculture automated monitoring systems using WSN. Journal of ambient intelligence and humanized computing, 12, 8009–8016 (2021) [Google Scholar]
- Z.M. Yaseen, Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of hydrology, 542, 603–614 (2016) [CrossRef] [Google Scholar]
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