Summary of Machine Learning For Asymptomatic Ratoon Stunting Disease Detection with Freely Available Satellite Based Multispectral Imaging, by Ethan Kane Waters et al.
Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging
by Ethan Kane Waters, Carla Chia-ming Chen, Mostafa Rahimi Azghadi
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 machine learning approach to detect asymptomatic infectious diseases, specifically Ratoon Stunting Disease (RSD), in different sugarcane varieties using freely available satellite-based spectral data. The study employs various algorithms, including Support Vector Machine with Radial Basis Function Kernel (SVM-RBF), Gradient Boosting, Random Forest, Logistic Regression, and Quadratic Discriminant Analysis. The results show that SVM-RBF and Gradient Boosting achieve high classification accuracy, while Logistic Regression and Quadratic Discriminant Analysis exhibit variable performance across different varieties. The inclusion of sugarcane variety and vegetation indices is crucial for accurate disease detection, consistent with existing literature. This study demonstrates the potential of satellite-based remote sensing as a cost-effective alternative to traditional laboratory testing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us detect diseases in sugarcane, which is important for growing crops well. The researchers used computer programs to look at pictures taken from space and find signs of a disease called Ratoon Stunting Disease (RSD). They tested different ways of doing this and found that one method was really good at finding the disease. This is helpful because it’s cheaper and faster than sending samples to a lab. |
Keywords
* Artificial intelligence * Boosting * Classification * Logistic regression * Machine learning * Random forest * Support vector machine
