Leafy Spurge Dataset: Real-world Weed Classification Within Aerial Drone Imagery
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914788217454592 |
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| author | Doherty, Kyle Gurinas, Max Samsoe, Erik Casper, Charles Larkin, Beau Ramsey, Philip Trabucco, Brandon Salakhutdinov, Ruslan |
| author_facet | Doherty, Kyle Gurinas, Max Samsoe, Erik Casper, Charles Larkin, Beau Ramsey, Philip Trabucco, Brandon Salakhutdinov, Ruslan |
| contents | Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of North America from Eastern Europe. When paired with contemporary computer vision systems, unmanned aerial vehicles, or drones, offer the means to track expansion of problem plants, such as leafy spurge, and improve chances of controlling these weeds. We gathered a dataset of leafy spurge presence and absence in grasslands of western Montana, USA, then surveyed these areas with a commercial drone. We trained image classifiers on these data, and our best performing model, a pre-trained DINOv2 vision transformer, identified leafy spurge with 0.84 accuracy (test set). This result indicates that classification of leafy spurge is tractable, but not solved. We release this unique dataset of labelled and unlabelled, aerial drone imagery for the machine learning community to explore. Improving classification performance of leafy spurge would benefit the fields of ecology, conservation, and remote sensing alike. Code and data are available at our website: leafy-spurge-dataset.github.io. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_03702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leafy Spurge Dataset: Real-world Weed Classification Within Aerial Drone Imagery Doherty, Kyle Gurinas, Max Samsoe, Erik Casper, Charles Larkin, Beau Ramsey, Philip Trabucco, Brandon Salakhutdinov, Ruslan Computer Vision and Pattern Recognition Machine Learning Invasive plant species are detrimental to the ecology of both agricultural and wildland areas. Euphorbia esula, or leafy spurge, is one such plant that has spread through much of North America from Eastern Europe. When paired with contemporary computer vision systems, unmanned aerial vehicles, or drones, offer the means to track expansion of problem plants, such as leafy spurge, and improve chances of controlling these weeds. We gathered a dataset of leafy spurge presence and absence in grasslands of western Montana, USA, then surveyed these areas with a commercial drone. We trained image classifiers on these data, and our best performing model, a pre-trained DINOv2 vision transformer, identified leafy spurge with 0.84 accuracy (test set). This result indicates that classification of leafy spurge is tractable, but not solved. We release this unique dataset of labelled and unlabelled, aerial drone imagery for the machine learning community to explore. Improving classification performance of leafy spurge would benefit the fields of ecology, conservation, and remote sensing alike. Code and data are available at our website: leafy-spurge-dataset.github.io. |
| title | Leafy Spurge Dataset: Real-world Weed Classification Within Aerial Drone Imagery |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.03702 |