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Multi-Spectral Imaging for Weed Identification in Herbicides Testing
dc.contributor.author | Agüera Vega, Francisco | |
dc.contributor.author | Carvajal Ramírez, Fernando | |
dc.contributor.author | Martínez Carricondo, Patricio Jesús | |
dc.contributor.author | Martín Garzón, Gracia Ester | |
dc.contributor.author | Ortega López, Gloria | |
dc.contributor.author | Ortega López, Luis | |
dc.date.accessioned | 2024-01-22T07:57:32Z | |
dc.date.available | 2024-01-22T07:57:32Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1854-3871 | |
dc.identifier.uri | http://hdl.handle.net/10835/15268 | |
dc.description.abstract | new methodology to help to improve the efficiency of herbicide assessment is explained. It consists of an automatic tool to quantify the percentage of weeds and plants of interest (sunflowers) that are present in a given area. Images of the crop field taken from Sequoia camera were used. Firstly, the quality of the images of each band is improved. Later, the resulting multispectral images are classified into several classes (soil, sunflower and weed) through a novel algorithm implemented in e-Cognition software. Obtained results of the proposed classifications have been compared with two deep learning-based segmentation methods (U-Net and FPN). | es_ES |
dc.language.iso | en | es_ES |
dc.subject | multi-spectral images | es_ES |
dc.subject | multi-spectral classification | es_ES |
dc.subject | herbicide assessment | es_ES |
dc.subject | deep learning segmentation | es_ES |
dc.subject | e-Cognition | es_ES |
dc.title | Multi-Spectral Imaging for Weed Identification in Herbicides Testing | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://doi.org/10.15388/22-INFOR498 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |