Optimización de la evaluación de pastos mediante la implementación de imágenes multiespectrales y vehículo aéreo no tripulado

Diego Alfonso Pelaez Carrillo; Oscar Eduardo Gualdron Guerrero; Ivaldo Torres Chavez

doi:10.24054/rcta.v1i43.2850

Authors

Diego Alfonso Pelaez Carrillo Universidad de Pamplona https://orcid.org/0000-0001-5275-606X
Oscar Eduardo Gualdron Guerrero Universidad de Pamplona https://orcid.org/0000-0002-7854-6842
Ivaldo Torres Chavez Universidad de Pamplona https://orcid.org/0000-0003-4409-2616

DOI:

https://doi.org/10.24054/rcta.v1i43.2850

Keywords:

Multispectral images, unmanned aerial vehicle, Reflectance analysis, Remote sensors, Vegetation indices, NDVI, precision agriculture

Abstract

This article develops a way to optimize pasture evaluation using unmanned aerial vehicles (UAVs) and multispectral image analysis. The research was carried out in the Municipality of Pamplona, Colombia, with the aim of understanding and documenting the growth and evolution of pasture in agricultural areas. The methodology used in the research included zoning the terrain to identify favorable conditions for the study, ensuring that suitable areas existed to observe pasture development and facilitate access to experimental instruments. Important aspects such as obtaining GPS points on the ground to create polygons that are arranged as the study area are highlighted, allowing for the planning of UAV flight missions, which in turn lead to the use of autonomous flight management software. The acquisition of multispectral images is made possible through the use of multispectral cameras integrated into the UAV, capable of recording information in multiple spectral bands within and outside the visible spectrum, such as near-infrared and red edge. Statistical analysis provided a detailed insight into agricultural conditions by revealing significant correlations between the Normalized Difference Vegetation Index (NDVI) and various soil parameters such as potassium (K) and phosphorus (P). This innovative method provides accurate data and visualizations that assist in making decisions regarding sustainable pasture management in the region.

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