Summary of Sugarcane Health Monitoring with Satellite Spectroscopy and Machine Learning: a Review, by Ethan Kane Waters et al.
Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review
by Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Signal Processing (eess.SP)
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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 This review focuses on the application of machine learning (ML) and satellite-based spectroscopy in sugarcane health monitoring and disease/pest detection. The paper discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, and traditional detection methods. It highlights the importance of considering variables such as crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety when assessing spectral reflectance for accurate health assessments. The review also explores the current literature on ML techniques and vegetation indices, noting a lack of comprehensive comparisons between the two. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sugarcane farming is important around the world, but it can be hard to keep an eye on how plants are doing without being there in person. Scientists have been using satellites to get a bird’s-eye view of crops, which can help identify problems like diseases or pests. This review looks at how machine learning and satellite-based spectroscopy can be used to monitor sugarcane health and detect problems early. It talks about the different things that can affect how plants reflect light from space, like what kind of soil they’re growing in or how much water they have. |
Keywords
» Artificial intelligence » Machine learning
