Summary of Explainability Of Sub-field Level Crop Yield Prediction Using Remote Sensing, by Hiba Najjar et al.
Explainability of Sub-Field Level Crop Yield Prediction using Remote Sensing
by Hiba Najjar, Miro Miranda, Marlon Nuske, Ribana Roscher, Andreas Dengel
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 study focuses on developing predictive models for soybean, wheat, and rapeseed crop yields in Argentina, Uruguay, and Germany using deep learning techniques. The researchers employ a long short-term memory network and investigate the impact of different temporal samplings of satellite data and adding additional relevant modalities. To establish trust in the models, they also analyze the interaction between the input data and model decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to create reliable crop yield prediction models for soybean, wheat, and rapeseed crops. Researchers use deep learning techniques with a large dataset of satellite images, additional data modalities, and crop yield maps. They want to understand how different parts of the images help predict yields and identify important growth stages. |
Keywords
* Artificial intelligence * Deep learning
