<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Natalia Arteaga-López</style></author><author><style face="normal" font="default" size="100%">Carlos Delgado-Calvache</style></author><author><style face="normal" font="default" size="100%">Juan-fernando Casanova</style></author><author><style face="normal" font="default" size="100%">Cristhian Figueroa</style></author><author><style face="normal" font="default" size="100%">Christian Nicolás Figueroa Martínez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Coffee Crops Analysis Using UAVs Equipped with Multispectral Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Revista Facultad De Ingenieria Uptc</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">coffee</style></keyword><keyword><style  face="normal" font="default" size="100%">multispectral images</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic data</style></keyword><keyword><style  face="normal" font="default" size="100%">UAV</style></keyword><keyword><style  face="normal" font="default" size="100%">vegetation indices</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://revistas.uptc.edu.co/index.php/ingenieria/article/view/14870/12367</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">31(62)</style></volume><pages><style face="normal" font="default" size="100%">1-15</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 Joaqu&amp;iacute;n, 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&amp;mdash;together with the chlorophyll values&amp;mdash;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.&lt;/p&gt;
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