Summary of Leaf Angle Estimation Using Mask R-cnn and Letr Vision Transformer, by Venkat Margapuri et al.
Leaf Angle Estimation using Mask R-CNN and LETR Vision Transformer
by Venkat Margapuri, Prapti Thapaliya, Trevor Rife
First submitted to arxiv on: 1 Aug 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 This study investigates the relationship between high-yielding crop varieties and plant leaf angles. The research finds that plants with upright leaf angles receive more light, leading to increased photosynthesis. To streamline plant parameter measurements in the field, the authors develop a computer vision pipeline combining Mask R-CNN and Line Segment Transformer. This approach is tested on two datasets containing 1,827 plant images collected using FieldBook. The estimated leaf angles are compared to manual measurements using ImageJ, achieving high similarity scores (0.98). This study demonstrates the feasibility of this pipeline for on-site leaf angle measurement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how plants with certain leaf shapes grow better. It finds that when leaves are more upright, they get more sunlight and photosynthesize faster. To make it easier to measure plant traits in the field, scientists use special cameras and computer programs. They test their method on lots of images taken by a mobile app called FieldBook. By comparing their results with manual measurements, they show that this approach works well. |
Keywords
* Artificial intelligence * Cnn * Mask * Transformer
