Summary of Cmavit: Integrating Climate, Managment, and Remote Sensing Data For Crop Yield Estimation with Multimodel Vision Transformers, by Hamid Kamangir et al.
CMAViT: Integrating Climate, Managment, and Remote Sensing Data for Crop Yield Estimation with Multimodel Vision Transformers
by Hamid Kamangir, Brent. S. Sams, Nick Dokoozlian, Luis Sanchez, J. Mason. Earles
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A new deep learning-based multi-model called Climate-Management Aware Vision Transformer (CMAViT) is introduced for predicting crop yields at the pixel level. This model integrates spatial and temporal data from remote sensing imagery, short-term meteorological data, and text-represented management practices using a cross-attention encoder. CMAViT outperforms traditional models like UNet-ConvLSTM in capturing spatial variability and yield prediction, particularly for extreme values. The model achieved an R2 of 0.84 and a MAPE of 8.22% on an unseen test dataset. Excluding specific modalities (management practices, climate data, or both) lowered performance, highlighting each modality’s importance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CMAViT is a new way to predict crop yields using deep learning. It combines different types of information like weather, farm management, and pictures from the sky to make more accurate predictions. This helps farmers plan better and grow more food. The model does a great job of predicting yields, especially when things are extreme. If you remove some of the information it uses, its performance drops. |
Keywords
» Artificial intelligence » Cross attention » Deep learning » Encoder » Unet » Vision transformer
