Summary of The Effect Of Different Optimization Strategies to Physics-constrained Deep Learning For Soil Moisture Estimation, by Jianxin Xie et al.
The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation
by Jianxin Xie, Bing Yao, Zheyu Jiang
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed physics-constrained deep learning (P-DL) framework integrates physics-based principles on water transport and water sensing signals for effective reconstruction of soil moisture dynamics. The model aims to improve agricultural production and crop yield through precision irrigation and farming tools. By adopting different optimizers, including Adam, RMSprop, and GD, the P-DL framework minimizes the loss function during training. In a case study, Adam optimizers empirically outperform other methods in both mini-batch and full-batch training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Soil moisture is important for agriculture and the environment. Accurate modeling and monitoring of soil moisture helps improve crop yield through precision irrigation and farming tools. A new framework uses deep learning to reconstruct soil moisture dynamics, combining physical principles with sensor data. This improves our ability to understand and manage soil moisture. The framework tests different methods to find the best one for training. |
Keywords
* Artificial intelligence * Deep learning * Loss function * Precision
