Coffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS images
Laen...
KuupΓ€ev
2020
KΓ€ttesaadav alates
Autorid
Santos, L.M.
Ferraz, G.A.S.
Diotto, A.V.
Barbosa, B.D.S.
Maciel, D.T.
Andrade, M.T.
Ferraz, P.F.P.
Rossi, G.
Ajakirja pealkiri
Ajakirja ISSN
KΓΆite pealkiri
Kirjastaja
Abstrakt
Because of different Brazilian climatic conditions and the different plant conditions,
such as the stage of development and even the variety, wide variation may exist in the crop
coefficients (πΎπ) values, both spatially and temporally. Thus, the objective of this study was to
develop a methodology to determine the short-term πΎπ using biophysical parameters of coffee
plants detected images obtained by an Unmanned Aircraft System (UAS). The study was
conducted in Travessia variety coffee plantation. A UAS equipped with a digital camera was
used. The images were collected in the field and were processed in Agisoft PhotoScan software.
The data extracted from the images were used to calculate the biophysical parameters: leaf area
index (LAI), leaf area (LA) and πΎπ. GeoDA software was used for mapping and spatial analysis.
The pseudo-significance test was applied with p < 0.05 to validate the statistic. Moran's index (I)
for June was 0.228 and for May was 0.286. Estimates of πΎπ values in June varied between 0.963
and 1.005. In May, the πΎπ values were 1.05 for 32 blocks. With this study, a methodology was
developed that enables the estimation of πΎπ using remotely generated biophysical crop data.
Kirjeldus
MΓ€rksΓ΅nad
Coffea arabica L., drone, irrigation, leaf area, uav (unmanned aerial vehicle), articles