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The objective of this research was to study the spatial variability of NDVI and chlorophyll sampled by different sensors, as well as to evaluate the correlation between them in a coffee field. The study was carried out on a coffee farm located in the municipality of Três Pontas, Minas Gerais. A sampling grid containing 30 points was created for the study area. Each sampling point was represented by one plant, which was georeferenced by a GNSS RTK. For each sample plant, NDVI and chlorophyll were obtained by the optical and active sensors GreenSeeker and ClorofiLOG, respectively. In addition, it was carried out a flight with an RPA equipped with a passive and multispectral sensor. Using the data obtained by active sensors, a geostatistical analysis was carried out to evaluate the spatial variability of NDVI and chlorophyll. The geostatistical analysis verified the existence of spatial dependence for the two attributes, and thus it was possible to generate spatialization maps through kriging. The images obtained by the passive sensor resulted in five multispectral orthomosaics, making it possible to calculate the NDVI, thus generating a spatialization map of this index. It was possible to observe in the generated maps, points that presented a certain similarity and for this purpose a correlation analysis was carried out for the values of each attribute, sampled directly in the maps, and in different sampling grids (30, 60, 90 and 120 points). By analyzing the Pearson coefficient (R) it was possible to quantify the level of correlation between the data obtained by the different sensors and through the t test it was possible to find significant correlations between them. [ABSTRACT FROM AUTHOR]

The coffee grower seeks to increase productivity, as well as reduce the operating costs of his crop. Precision Agriculture (PA) is composed of a cycle of tools and technologies that can bring a good return to coffee growers, seeking to optimize production processes, bringing better yields and minimizing costs. Therefore, the objective of this research is to evaluate the spacetime behavior of productivity in a coffee plantation, aiming to apply AP techniques. The study was carried out in a coffee plantation of the species (Coffea arabica), cultivar Topázio MG1190, located in the municipality of Três Pontas, Brazil, with an area of 1.2 ha. With the aid of a GNSS RTK, 30 plants were georeferenced, from which their yields were later sampled in the years 2020, 2021 and 2022. The collected data were evaluated in two statistical processes in the RStudio software. The first stage consisted of a one-way analysis of variance with repeated measures, from the results it is concluded that there are differences between the production averages when buying the productivity of the years 2020, 2021 and 2022 and, in addition, the coefficient of variation for the three sets of samples was quite high (CV > 30%) indicating a heterogeneity between the data. The second stage consisted of a geostatistical analysis the data were fitted in a model and interpolated by ordinary kriging; the result was maps of spatial variability. Through these maps it was possible to evaluate the behavior of productivity spatially and temporally, as well as to quantify areas that had higher and lower levels of this attribute. It is concluded that productivity, even in the case of such a small productive area, can vary substantially in space and time, and the use of PA can help producers in decision making regarding management. [ABSTRACT FROM AUTHOR]