Instance segmentation for automated weeds and crops detection in farmlands
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| Format: | Artículo científico |
| Sprache: | en |
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Instituto Tecnológico de Costa Rica
2020
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| author | Adán Mora-Fallas |
| author_facet | Adán Mora-Fallas |
| contents | Instance segmentation for automated weeds and crops detection in farmlands Adán Mora-Fallas Hervé Goëau Alexis Joly Pierre Bonnet Erick Mata-Montero Ingeniería Deep learning weed detection computer vision instance segmentation precision agriculture Based on recent successful applications of Deep Learning techniques in classification, detection and segmentation of plants, we propose an instance segmentation approach that uses a Mask R-CNN model for weeds and crops detection on farmlands. We evaluated our model performance with the MSCOCO average precision metric, contrasting the use of data augmentation techniques. Results obtained show how the model fits very well in this context, opening new opportunities to automated weed control solutions, at larger scales. 2020 artículo científico 2215-3241 https://www.redalyc.org/articulo.oa?id=699878645003 en http://www.redalyc.org/revista.oa?id=6998 Tecnología en marcha application/pdf Instituto Tecnológico de Costa Rica Tecnología en marcha (Costa Rica) Num.CARLA Vol.33 |
| format | Artículo científico |
| id | redalyc_699878645003 |
| institution | Redalyc |
| language | en |
| publishDate | 2020 |
| publisher | Instituto Tecnológico de Costa Rica |
| spellingShingle | Instance segmentation for automated weeds and crops detection in farmlands Adán Mora-Fallas Ingeniería Deep learning weed detection computer vision instance segmentation precision agriculture Instance segmentation for automated weeds and crops detection in farmlands Adán Mora-Fallas Hervé Goëau Alexis Joly Pierre Bonnet Erick Mata-Montero Ingeniería Deep learning weed detection computer vision instance segmentation precision agriculture Based on recent successful applications of Deep Learning techniques in classification, detection and segmentation of plants, we propose an instance segmentation approach that uses a Mask R-CNN model for weeds and crops detection on farmlands. We evaluated our model performance with the MSCOCO average precision metric, contrasting the use of data augmentation techniques. Results obtained show how the model fits very well in this context, opening new opportunities to automated weed control solutions, at larger scales. 2020 artículo científico 2215-3241 https://www.redalyc.org/articulo.oa?id=699878645003 en http://www.redalyc.org/revista.oa?id=6998 Tecnología en marcha application/pdf Instituto Tecnológico de Costa Rica Tecnología en marcha (Costa Rica) Num.CARLA Vol.33 |
| title | Instance segmentation for automated weeds and crops detection in farmlands |
| topic | Ingeniería Deep learning weed detection computer vision instance segmentation precision agriculture |
| url | https://www.redalyc.org/articulo.oa?id=699878645003 |