Instance segmentation for automated weeds and crops detection in farmlands
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| Autores: | , , , , |
|---|---|
| Formato: | artículo original |
| Estado: | Versión publicada |
| Data de Publicación: | 2020 |
| Descripción: | 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. |
| País: | Portal de Revistas TEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Idioma: | Inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/5069 |
| Acceso en liña: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5069 |
| Palabra crave: | Deep learning instance segmentation computer vision precision agriculture biodiversity informatics weed detection species identification Aprendizaje profundo segmentación de instancias visión por computadora agricultura de precisión bioinformática detección de malezas identificación de especies |