Fecha/hora: 31/05/2024 23:20:57

Biblioteca(s): | INIA Las Brujas.
Fecha corriente: | 08/06/2022
Fecha de la última actualización: | 08/06/2022
Autoria: | CAL, A.; PRECIOZZI, J.; MUSÉ, PABLO
Afiliación: | ADRIAN TABARE CAL ALVAREZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JAVIER PRECIOZZI, IIE, Facultad de Ingeniería, Universidad de la Republica, Uruguay; Digital Sense, Uruguay; PABLO MUSÉ, IIE, Facultad de Ingeniería, Universidad de la Republica, Uruguay.
Título: | Automatic Classification of Agricultural Summer Crops in Uruguay. [Conference paper]
Complemento del título: | IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels (Belgium) 12-16 July 2021. Code 176845.
Año de publicación: | 2021
Fuente/Imprenta: | International Geoscience and Remote Sensing Symposium (IGARSS), 2021, pages 6520 - 6523. doi: http://doi.org/10.1109/IGARSS47720.2021.9555035
DOI: | 10.1109/IGARSS47720.2021.9555035
Idioma: | Inglés
Notas: | Publisher: Institute of Electrical and Electronics Engineers Inc. -- Sponsors: The Institute of Electrical and Electronics Engineers Geoscience and Remote Sensing Society (GRSS).
Contenido: | ABSTRACT - In this work, we present a study for the classification of summer crops on a nationwide perspective. Using both optical and radar satellite images, we implement a time-series classification algorithm using XGBoost. Two datasets with farm-level information were used: one with ground truth obtained directly from farmers' production and the other with declared crops obtained at the government level. The crops analyzed were corn, soybean, sorghum, and pastures. When trained and validated with ground truth, the classifier yields a F1-Score performance of 99% for soybean, and values higher than 80% for corn and sorghum. Predictions performed with this model on the dataset of declared crops lead to F1-Score values of 54, 97, and 50%, for corn, soybean, and sorghum, respectively. These low values for corn and sorghum indicate the presence of mislabeled data in that dataset, which in turns may suggest issues with the declarations provided by the farmers. ©2021 IEEE.
Palabras-Clave: | Data fusion; K-means; Laser radar; Radar imaging; Satellites; Soil preservation; Sustainable agriculture; XGBoost.
Categoría del tema: | A50 Investigación agraria
Registro original: | INIA Las Brujas (LB)
Ejemplares: | LB - (103091-1) - PXI - PC - DD - IGARSS 2021 -  -  - DIS