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Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee plantation using remotely piloted aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and 10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021). The highest correlation (51.57%) was observed between the green spectral band and the 0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to map the spatial variability of soil moisture, and the correlation between vegetation indices and soil moisture was evaluated. The results revealed a strong spatial dependence of soil moisture and significant correlations between vegetation indices and soil moisture, highlighting the effectiveness of RPA and geostatistics in assessing water conditions in coffee plantations. In addition to soil moisture, vegetation indices provided information about plant vigor, water stress, and general crop health.
Details
Data acquisition;
Plantations;
Water stress;
Soil moisture;
Coffee;
Spatial dependencies;
Altitude;
Productivity;
Water;
Soil water;
Irrigation;
Rainy season;
Radiation;
Evaluation;
Adaptability;
Seasons;
Geostatistics;
Agriculture;
Remote sensing;
Moisture content;
Spectrum analysis;
Aircraft;
Vegetation index;
Correlation;
Remotely piloted aircraft;
Dry season;
Crops;
Harvest
; Ferraz Gabriel Araújo e Silva 1
; Figueiredo, Vanessa Castro 2 ; Valente Gislayne Farias 1 ; Volpato Margarete Marin Lordelo 2 ; Machado Marley Lamounier 2 1 Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA), Lavras 37200-900, Brazil; [email protected] (S.A.d.S.S.); [email protected] (G.F.V.)
2 Agricultural Research Company of Minas Gerais, Belo Horizonte 31170-495, Brazil; [email protected] (V.C.F.); [email protected] (M.M.L.V.); [email protected] (M.L.M.)