Summary of Automating Grapevine Lai Features Estimation with Uav Imagery and Machine Learning, by Muhammad Waseem Akram et al.
Automating grapevine LAI features estimation with UAV imagery and machine learning
by Muhammad Waseem Akram, Marco Vannucci, Giorgio Buttazzo, Valentina Colla, Stefano Roccella, Andrea Vannini, Giovanni Caruso, Simone Nesi, Alessandra Francini, Luca Sebastiani
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); 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 develops an automated method for estimating the leaf area index (LAI) of grapevine plants using drone image data and machine learning models. Traditional LAI estimation methods are time-consuming, destructive, costly, and limited to a small scale. The proposed approach combines traditional feature extraction and deep learning techniques to enhance the performance of various machine learning models. Results show that deep learning-based feature extraction outperforms traditional methods. The new method offers faster, non-destructive, and cost-effective LAI calculation, enhancing precision agriculture practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses drones to take pictures of grapevines and helps farmers measure how healthy their plants are. Right now, farmers have to manually count the leaves on a small part of the vine, which is slow, expensive, and hurts the plant. The scientists developed a new way to do this using computers and special picture analysis techniques. They showed that this new method works better than older ways and can be used on a larger scale without hurting the plants. |
Keywords
» Artificial intelligence » Deep learning » Feature extraction » Machine learning » Precision
