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

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 values, both spatially and temporally. Thus, the objective of this study was to develop a methodology to determine the short-term 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. 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 values in June varied between 0.963 and 1.005. In May, the values were 1.05 for 32 blocks. With this study, a methodology was developed that enables the estimation of using remotely generated biophysical crop data.