Summary of Explainability Of Deep Learning-based Plant Disease Classifiers Through Automated Concept Identification, by Jihen Amara et al.
Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
by Jihen Amara, Birgitta König-Ries, Sheeba Samuel
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a method called Automated Concept-based Explanation (ACE) that helps improve the transparency and reliability of automatic plant disease detection systems. By applying ACE to the widely used InceptionV3 model and PlantVillage dataset, researchers aim to identify the critical features influencing model predictions and eliminate incidental biases that can compromise robustness. The approach is tested through systematic experiments, revealing both effective disease-related patterns and areas for targeted model improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how deep learning models can be made more transparent and reliable for detecting plant diseases. A new method called ACE helps explain why the model is making certain predictions by identifying important features in images of plants. This information can help make the models better at detecting diseases and reducing errors caused by things like background or lighting. The research suggests that this approach could lead to better tools for managing plant diseases in agriculture. |
Keywords
» Artificial intelligence » Deep learning
