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Coffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS images

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dc.contributor.authorSantos, L.M.
dc.contributor.authorFerraz, G.A.S.
dc.contributor.authorDiotto, A.V.
dc.contributor.authorBarbosa, B.D.S.
dc.contributor.authorMaciel, D.T.
dc.contributor.authorAndrade, M.T.
dc.contributor.authorFerraz, P.F.P.
dc.contributor.authorRossi, G.
dc.date.accessioned2020-05-11T15:57:50Z
dc.date.available2020-05-11T15:57:50Z
dc.date.issued2020
dc.description.abstractBecause 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 (𝐾𝑐) values, both spatially and temporally. Thus, the objective of this study was to develop a methodology to determine the short-term 𝐾𝑐 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 𝐾𝑐. 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 𝐾𝑐 values in June varied between 0.963 and 1.005. In May, the 𝐾𝑐 values were 1.05 for 32 blocks. With this study, a methodology was developed that enables the estimation of 𝐾𝑐 using remotely generated biophysical crop data.eng
dc.identifier.issn1406-894X
dc.identifier.publicationAgronomy Research, 2020, vol. 18, Special Issue 2, pp. 1463–1471eng
dc.identifier.urihttp://hdl.handle.net/10492/5699
dc.identifier.urihttps://doi.org/10.15159/ar.20.100
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ; openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCoffea arabica L.
dc.subjectdroneeng
dc.subjectirrigationeng
dc.subjectleaf areaeng
dc.subjectuav (unmanned aerial vehicle)eng
dc.subjectarticleseng
dc.titleCoffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS imageseng
dc.typeArticleeng

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2020, Vol. 18, Special Issue 2