Automating grapevine LAI features estimation with UAV imagery and machine learning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916497060790272 |
|---|---|
| author | Akram, Muhammad Waseem Vannucci, Marco Buttazzo, Giorgio Colla, Valentina Roccella, Stefano Vannini, Andrea Caruso, Giovanni Nesi, Simone Francini, Alessandra Sebastiani, Luca |
| author_facet | Akram, Muhammad Waseem Vannucci, Marco Buttazzo, Giorgio Colla, Valentina Roccella, Stefano Vannini, Andrea Caruso, Giovanni Nesi, Simone Francini, Alessandra Sebastiani, Luca |
| contents | The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17897 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automating grapevine LAI features estimation with UAV imagery and machine learning Akram, Muhammad Waseem Vannucci, Marco Buttazzo, Giorgio Colla, Valentina Roccella, Stefano Vannini, Andrea Caruso, Giovanni Nesi, Simone Francini, Alessandra Sebastiani, Luca Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning The leaf area index determines crop health and growth. Traditional methods for calculating it are time-consuming, destructive, costly, and limited to a scale. In this study, we automate the index estimation method using drone image data of grapevine plants and a machine learning model. Traditional feature extraction and deep learning methods are used to obtain helpful information from the data and enhance the performance of the different machine learning models employed for the leaf area index prediction. The results showed that deep learning based feature extraction is more effective than traditional methods. The new approach is a significant improvement over old methods, offering a faster, non-destructive, and cost-effective leaf area index calculation, which enhances precision agriculture practices. |
| title | Automating grapevine LAI features estimation with UAV imagery and machine learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2411.17897 |