Summary of An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification, by Aswini Kumar Patra et al.
An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification
by Aswini Kumar Patra, Ankit Varshney, Lingaraj Sahoo
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an explainable deep learning pipeline that uses vision transformers (ViTs) to detect early signs of drought stress in potato crops using aerial imagery. The authors combine ViT with support vector machine (SVM) or train a dedicated classification layer within ViT for end-to-end detection. Key findings include the visualization of attention maps, highlighting specific spatial features associated with drought stress. The proposed methods achieve high accuracy and provide interpretable insights for farmers to make informed decisions. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Drought can cause huge losses in crop production. To prevent this, it’s crucial to detect drought stress early on. This paper uses special computer models called vision transformers (ViTs) to analyze pictures of potato crops taken from above. The ViT model is combined with another technique called support vector machine (SVM) or trained to directly identify drought stress. The results show that the model can accurately detect drought stress and even explain why it made certain decisions. |
Keywords
» Artificial intelligence » Attention » Classification » Deep learning » Support vector machine » Vit