CAA. Revista UDCA Actualidad & Divulgación Científica · 2025

Drone-Integrated Geospatial Methodology for Carrot Crop Production

Understanding the spatial variability of soil is essential for mitigating negative externalities associated with crop production and management. This study proposes an integrated methodology for soil data acquisition and processing using a carrot crop as a case study, considering three phenological stages. Data were collected through a multivariable sensing device and RGB drone imagery, processed with WebODM to generate orthomosaics for each stage. Geographic coordinates were managed in QGIS, while geostatistical analysis and vegetation indices were computed using R and RStudio. Output files in TIFF format were integrated into QGIS for geographical interpretation. Results indicate a maximum Euclidean sampling distance of 6 m for the variogram assessment, with variations between 3 and 6 m across seven soil variables and three crop stages (s1, s2, s3). The p roposed m ethodology d emonstrates a significant a dvantage o ver t raditional a pproaches b y m erging predictions and graphic outputs, reducing uncertainty and processing time by 50%. Its replicability across diverse soil, crop, and climate conditions highlights its potential for improving decision-making in precision agriculture.