Summary of Corn Ear Detection and Orientation Estimation Using Deep Learning, by Nathan Sprague et al.
Corn Ear Detection and Orientation Estimation Using Deep Learning
by Nathan Sprague, John Evans, Michael Mardikes
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposed system uses computer vision to detect, track, and predict ear orientation in image sequences, allowing for accurate measurement of maize plant growth behavior. This automates manual measurements, reducing time and human error. The system leverages an object detector with keypoint detection, achieving 90% detection accuracy. Mean absolute error (MAE) in cardinal estimation is 18 degrees, similar to the 15-degree difference between two people measuring by hand. These results demonstrate the feasibility of computer vision for monitoring maize growth, opening up opportunities for further research and potential efficiencies in maize production. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps farmers grow healthier crops by using computers to track ear orientation in corn plants. Currently, people count ears by hand, which is slow and can be wrong. The new system uses a camera to take pictures of the ears, then calculates where they are pointing. It works well, detecting 9 out of 10 ears correctly. This means farmers can focus on making their crops better instead of counting them. |
Keywords
» Artificial intelligence » Mae
