Smart Water Resource Management by Analyzing the Soil Structure and Moisture Using Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3575-6_68
Reference8 articles.
1. Yu, J., et al.: A Deep learning approach for multi-depth soil water content prediction in summer maize growth period. IEEE Access 8, 199097–199110 (2020). https://doi.org/10.1109/ACCESS.2020.3034984
2. Yang, Y., et al.: A reliable soil moisture sensing methodology for agricultural irrigation. 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications(ISPA/IUCC) (2017)
3. Wang, T., Liang, J., Liu, X.: Soil moisture retrieval algorithm based on TFA and CNN. IEEE Access 7, 597–604 (2019). https://doi.org/10.1109/ACCESS.2018.2885565
4. Sobayo, R., et al.: Integration of convolutional neural network and thermal images into soil moisture estimation. 2018 1st International Conference on Data Intelligence and Security (2018)
5. Xu, J., Luo, Y., Zhang, K., Yu, H.: Experimental study on convolutional neural network-based soil moisture feature recognition. 2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE), pp. 597–600 (2019). https://doi.org/10.1109/EITCE47263.2019.9095171
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