Significance of AI in Smart Agriculture: Methods, Technologies, Trends, and Challenges
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53972-5_1
Reference32 articles.
1. Y. Liu, X. Ma, L. Shu, G. P. Hancke, and A. M. Abu-Mahfouz, From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges, IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4322–4334, Jun. 2021, https://doi.org/10.1109/tii.2020.3003910.
2. S. Koul, Machine learning and deep learning in agriculture, Smart Agriculture, pp. 1–19, Jan. 2021, https://doi.org/10.1201/b22627-1.
3. R. Taghizadeh-Mehrjardi, K. Nabiollahi, L. Rasoli, R. Kerry, and T. Scholten, Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models,” Agronomy, vol. 10, no. 4, p. 573, Apr. 2020, https://doi.org/10.3390/agronomy10040573.
4. M. Doshi and A. Varghese, Smart agriculture using renewable energy and AI-powered IoT, AI, Edge and IoT-based Smart Agriculture, pp. 205–225, 2022, https://doi.org/10.1016/b978-0-12-823694-9.00028-1.
5. D. Siddharth, D. K. Saini, and A. Kumar, Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0, Unmanned Aerial Vehicles for Internet of Things (IoT), pp. 247–276, Jul. 2021, https://doi.org/10.1002/9781119769170.ch14.
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