Summary of Detection Of Spider Mites on Labrador Beans Through Machine Learning Approaches Using Custom Datasets, by Violet Liu et al.
Detection of Spider Mites on Labrador Beans through Machine Learning Approaches Using Custom Datasets
by Violet Liu, Jason Chen, Ans Qureshi, Mahla Nejati
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 novel visual machine learning approach for early plant disease detection is proposed, leveraging RGB and NIR data from a JAI FS-1600D-10GE camera to build an RGBN dataset. A two-stage model featuring YOLOv8 and sequential CNNs is used to train on partially labeled data, demonstrating improved performance compared to single-stage end-to-end segmentation models. The sequential CNN achieves high validation accuracy using RGBN data, outperforming RGB-based methods in classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help farmers detect plant diseases earlier has been developed. This method uses special cameras that take pictures in different ways (like a normal camera and a thermal camera). The pictures are then used to train a computer program to recognize when a plant is sick. The program is much better at recognizing disease than previous methods, which can help farmers catch problems before they spread. |
Keywords
» Artificial intelligence » Classification » Cnn » Machine learning
