Smart Agriculture Using an Enhanced Selection for MPRs
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-35248-5_26
Reference20 articles.
1. Suryo, P.S.B.C., Mustika, I.W., Wahyunggoro, O., Wasisto, H.S.: Improved time series prediction using LSTM neural network for smart agriculture application. In: 2019 5th International Conference on Science and Technology (ICST), pp. 1–4 (2019). https://doi.org/10.1109/ICST47872.2019.9166401
2. Rahman, M.U., Baiardi, F., Ricci, L.: Blockchain smart contract for scalable data sharing in iot: a case study of smart agriculture. In: 2020 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), pp. 1–7 (2020). https://doi.org/10.1109/GCAIoT51063.2020.9345874
3. Kassim, M.R.M.: IoT applications in smart agriculture: issues and challenges. IEEE Conference on Open Systems (ICOS) 2020, 19–24 (2020). https://doi.org/10.1109/ICOS50156.2020.9293672
4. Charoenpanyasak, S., Suntiamorntut, W., Phatthanatraiwat, T., Ruksachum, J.: Smart shrimp hatchery using Mikros platform. In: 2011 4th Joint IFIP Wireless and Mobile Networking Conference (WMNC 2011), pp. 1–5 (2011). https://doi.org/10.1109/WMNC.2011.6097227
5. Alex, N., Ali, J., Sobin, C.C.: Sustainability and impactness of smart-agri architecture on environment. In: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 574–578 (2021). https://doi.org/10.1109/MASS52906.2021.00081
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