Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras
The use of Unmanned Aerial Vehicles (UAVs) equipped with spectral cameras has increased in recent years, especially in the agricultural sector, because it allows farmers and researchers to analyze the state of a crop, i.e., health, nutrients, growth, epidemics, among other parameters. In Colombia, t...
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| Format: | Online |
| Language: | eng |
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Universidad Pedagógica y Tecnológica de Colombia
2022
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| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870 |
| _version_ | 1801706101186494464 |
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| author | Arteaga-López, Natalia Delgado-Calvache, Carlos Casanova, Juan-Fernando Figeroa, Cristian |
| author_facet | Arteaga-López, Natalia Delgado-Calvache, Carlos Casanova, Juan-Fernando Figeroa, Cristian |
| author_sort | Arteaga-López, Natalia |
| collection | OJS |
| description | The use of Unmanned Aerial Vehicles (UAVs) equipped with spectral cameras has increased in recent years, especially in the agricultural sector, because it allows farmers and researchers to analyze the state of a crop, i.e., health, nutrients, growth, epidemics, among other parameters. In Colombia, the coffee production sector faces several challenges, such as the need to increase the productivity, the yield, and the quality of coffee. This work estimated the health status of a Castilla variety crop located in San Joaquín, Tambo, Cauca to support the decision-making of coffee growers. For this, chlorophyll data were measured in the field with the CCM-200 plus device, multispectral images were captured with the MAPIR SURVEY 3 camera airborne on a SOLO 3DR UAV, and synthetic data were generated to increase the data set. Six vegetation indices were set, which—together with the chlorophyll values—were modeled through the implementation of simple and multiple linear regressions, decision trees, vector machines, random forests, and k-nearest neighbors. The model with the best performance and the lowest mean square error was disorder with the support vector machine. Likewise, the best performance indices in the models were CVI, GNDVI, and GCI, which are widely used in agriculture to estimate the chlorophyll of plants. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-14870 |
| institution | Revista Facultad de Ingeniería |
| language | eng |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-148702023-05-31T16:24:11Z Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras Uso de VANTs equipados con cámaras multiespectrales para el análisis de cultivos de café Arteaga-López, Natalia Delgado-Calvache, Carlos Casanova, Juan-Fernando Figeroa, Cristian agriculture coffee multispectral images synthetic data vegetation index UAV agricultura café imagenes multiespectrles datos sintéticos índices de vegetación The use of Unmanned Aerial Vehicles (UAVs) equipped with spectral cameras has increased in recent years, especially in the agricultural sector, because it allows farmers and researchers to analyze the state of a crop, i.e., health, nutrients, growth, epidemics, among other parameters. In Colombia, the coffee production sector faces several challenges, such as the need to increase the productivity, the yield, and the quality of coffee. This work estimated the health status of a Castilla variety crop located in San Joaquín, Tambo, Cauca to support the decision-making of coffee growers. For this, chlorophyll data were measured in the field with the CCM-200 plus device, multispectral images were captured with the MAPIR SURVEY 3 camera airborne on a SOLO 3DR UAV, and synthetic data were generated to increase the data set. Six vegetation indices were set, which—together with the chlorophyll values—were modeled through the implementation of simple and multiple linear regressions, decision trees, vector machines, random forests, and k-nearest neighbors. The model with the best performance and the lowest mean square error was disorder with the support vector machine. Likewise, the best performance indices in the models were CVI, GNDVI, and GCI, which are widely used in agriculture to estimate the chlorophyll of plants. El uso de Vehículos Aéreos No Tripulados (UAVs) equipados con cámaras espectrales se ha incrementado en los últimos años, especialmente en el sector agrícola ya que permite a los agricultores e investigadores analizar el estado de un cultivo, ya sea para analizar su salud, nutrientes, crecimiento, epidemias, entre otros parámetros. En Colombia, el sector cafetero enfrenta varios desafíos, como la necesidad de incrementar la productividad, el rendimiento y la calidad del café. Este trabajo estimó el estado sanitario de un cultivo variedad Castilla ubicado en San Joaquín, Tambo, Cauca para apoyar la toma de decisiones de los caficultores. Para ello, se midieron datos de clorofila en campo con el dispositivo CCM-200 plus, se capturaron imágenes multiespectrales con la cámara MAPIR SURVEY 3 aerotransportada en un UAV SOLO 3DR y se generaron datos sintéticos para aumentar el conjunto de datos. Se establecieron seis índices de vegetación, los cuales, junto con los valores de clorofila, se modelaron mediante la implementación de regresiones lineales simples y múltiples, árboles de decisión, máquinas vectoriales, bosques aleatorios y k-vecinos más cercanos. El modelo con el mejor rendimiento y el menor error cuadrático medio fue el modelo implementado con máquina de vectores de soporte. De igual forma, los mejores índices de desempeño en los modelos fueron CVI, GNDVI y GCI, los cuales son muy utilizados en agricultura para estimar la clorofila de las plantas. Universidad Pedagógica y Tecnológica de Colombia 2022-11-27 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870 10.19053/01211129.v31.n62.2022.14870 Revista Facultad de Ingeniería; Vol. 31 No. 62 (2022): October-December 2022 (Continuous Publication); e14870 Revista Facultad de Ingeniería; Vol. 31 Núm. 62 (2022): Octubre-Diciembre 2022 (Publicación Continua) ; e14870 2357-5328 0121-1129 eng https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870/12367 https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870/12572 Copyright (c) 2022 Natalia Arteaga-López, Carlos Delgado-Calvache, Juan-Fernando Casanova, Cristian Figeroa http://creativecommons.org/licenses/by/4.0 |
| spellingShingle | agriculture coffee multispectral images synthetic data vegetation index UAV agricultura café imagenes multiespectrles datos sintéticos índices de vegetación Arteaga-López, Natalia Delgado-Calvache, Carlos Casanova, Juan-Fernando Figeroa, Cristian Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title | Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title_alt | Uso de VANTs equipados con cámaras multiespectrales para el análisis de cultivos de café |
| title_full | Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title_fullStr | Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title_full_unstemmed | Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title_short | Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras |
| title_sort | coffee crops analysis using uavs equipped with multispectral cameras |
| topic | agriculture coffee multispectral images synthetic data vegetation index UAV agricultura café imagenes multiespectrles datos sintéticos índices de vegetación |
| topic_facet | agriculture coffee multispectral images synthetic data vegetation index UAV agricultura café imagenes multiespectrles datos sintéticos índices de vegetación |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870 |
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