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| Format: | Recurso digital |
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2025
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| Online Access: | https://doi.org/10.20350/digitalCSIC/17389 |
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| _version_ | 1866901595742011392 |
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| author | Emmi, Luis Fernandez, Roemi |
| author_facet | Emmi, Luis Fernandez, Roemi |
| contents | <p>The data were acquired using the RGB camera TRI032S-CC RGB from Lucid Vision Labs equipped with the SV- 04514V lens (resolution: 5 MP; FoV: 59.4° × 79°). Two cameras were used, one on the right and one on the left of a mobile robotic platform, which navigated autonomously thro2.<br>Image Acquisition: RGB images were captured (left and right cameras) in a vineyard environment. Each image was stored in both high-quality (HQ) and low-quality (LQ) formats. - Image Preprocessing: Images were either used in full resolution or cropped to focus on specific regions of interest. Additionally, ORB (Oriented FAST and Rotated BRIEF) feature detection was applied to extract keypoints for targeted analysis. - Species Identification: Each image was submitted to the Pl@ntNet API for automated plant species identification. The API returns a ranked list of candidate species with associated confidence scores. The raw responses were stored in .pkl files, separately for left and right cameras. - Data Structuring: The Pl@ntNet responses were organized into Python dictionaries, indexed by image filename, and saved in structured folders based on project, image quality, and analysis method. Certain images were subjected to blurring in specific regions to anonymize individuals and obscure sensitive information, including vehicle license plates, in compliance with data protection and ethical guidelines.ugh a vineyard, obtaining images of the intra-row space.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_20350_digitalCSIC_17389 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | High- and Low-Quality RGB Image Dataset for Weed Species Identification in Vineyards Emmi, Luis Fernandez, Roemi <p>The data were acquired using the RGB camera TRI032S-CC RGB from Lucid Vision Labs equipped with the SV- 04514V lens (resolution: 5 MP; FoV: 59.4° × 79°). Two cameras were used, one on the right and one on the left of a mobile robotic platform, which navigated autonomously thro2.<br>Image Acquisition: RGB images were captured (left and right cameras) in a vineyard environment. Each image was stored in both high-quality (HQ) and low-quality (LQ) formats. - Image Preprocessing: Images were either used in full resolution or cropped to focus on specific regions of interest. Additionally, ORB (Oriented FAST and Rotated BRIEF) feature detection was applied to extract keypoints for targeted analysis. - Species Identification: Each image was submitted to the Pl@ntNet API for automated plant species identification. The API returns a ranked list of candidate species with associated confidence scores. The raw responses were stored in .pkl files, separately for left and right cameras. - Data Structuring: The Pl@ntNet responses were organized into Python dictionaries, indexed by image filename, and saved in structured folders based on project, image quality, and analysis method. Certain images were subjected to blurring in specific regions to anonymize individuals and obscure sensitive information, including vehicle license plates, in compliance with data protection and ethical guidelines.ugh a vineyard, obtaining images of the intra-row space.</p> |
| title | High- and Low-Quality RGB Image Dataset for Weed Species Identification in Vineyards |
| url | https://doi.org/10.20350/digitalCSIC/17389 |