Summary of Adaptive Fusion Of Multi-view Remote Sensing Data For Optimal Sub-field Crop Yield Prediction, by Francisco Mena et al.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
by Francisco Mena, Deepak Pathak, Hiba Najjar, Cristhian Sanchez, Patrick Helber, Benjamin Bischke, Peter Habelitz, Miro Miranda, Jayanth Siddamsetty, Marlon Nuske, Marcela Charfuelan, Diego Arenas, Michaela Vollmer, Andreas Dengel
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 multi-view learning approach uses a combination of satellite images, weather data, and static features like soil properties and topographic information to predict crop yields for different crops (soybean, wheat, rapeseed) and regions. The model, called Multi-View Gated Fusion (MVGF), incorporates dedicated view-encoders and a Gated Unit (GU) module to effectively fuse the data. By learning a view-specific representation, the MVGF model outperforms conventional models on crop yield prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict how well crops will grow based on information from satellites, weather stations, and the soil. They used this data to make predictions for different types of crops in different regions. Their method, called Multi-View Gated Fusion (MVGF), did better than other methods at predicting crop yields. |
