Summary of Improved Cotton Leaf Disease Classification Using Parameter-efficient Deep Learning Framework, by Aswini Kumar Patra et al.
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework
by Aswini Kumar Patra, Tejashwini Gajurel
First submitted to arxiv on: 23 Dec 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 paper proposes an innovative deep learning framework to improve the identification of leaf-affecting diseases in cotton crops. The framework combines MobileNet’s trainable layers, transfer learning, data augmentation, and early stopping mechanisms. It achieves exceptional performance, accurately classifying seven cotton disease types with an overall accuracy of 98.42% and class-wise precision ranging from 96% to 100%. This model outperforms recent approaches in terms of both accuracy and model complexity, making it suitable for real-world applications in smart farming. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps farmers grow cotton better by creating a new way to identify diseases that affect leaves. The method uses machine learning techniques, which are already used in many areas like image recognition. The new approach works really well, correctly identifying seven different types of diseases 98.42% of the time! This is much better than what other methods have achieved. The best part is that this method is efficient and can be used by farmers to make their work easier. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Early stopping » Machine learning » Precision » Transfer learning
