The use of irrigation has expanded and favored agricultural productivity in recent years. The mapping through remote sensing has contributed to the monitoring of irrigated areas. In this sense, the objective of this study was to evaluate the central pivotal evolution in terms of location by municipalities, micro basins, soils and slope in the Goian tributary watershed of the Araguaia River State of Goiás. Data were available between 2000 and 2016. Irrigated areas were surveyed through the database available in the Geographical Information System of the State of Goiás (SGEI). The vector and raster data were manipulated using the Qgis v software. 2.18.26 (QGIS Development, 2019). The pivots were counted through the statistical function of the software. From the shape SGEI available in the soil map of classes is generated by categorizes tion of soil types. The declivity map was generated from raster files acquired through the Brazilian Geo morphological Database (INPE, 2017). The slope classes (%) were extracted with slope tool. There is an increase of more than 95% in the number of pivots and irrigated area between the years 2000 and 2016. The central pivots are more concentrated in the central region of the Red and Red-Light basins. The highest concentration of central pivots occurred in the municipality of Jussara. The pivots are located predominantly in an Oxisol area with a slope of 3 to 13%.

The study evaluated the efficacy and soybean spectral responses to fifteen foliar fungicide mixtures labeled to control Asian soybean rust. Canopy level reflectance was measured using a multispectral camera onboard a multirotor drone before and two hours after each spray. The third application of fungicides improved control of soybean rust and increased yield. Nevertheless, up to three consecutive foliar fungicides applications did not affect the reflectance of soybean plants at visible and infrared wavelengths. Thus, drones can be a viable strategy for data acquisition regardless of the application of the fungicides.

Often the producer does not know the exact number of fruit trees on his property or is unaware over the years due to the death of many plants. As a result, in order to avoid the need for a field trip for manual counting, this research aimed to use a model matching algorithm in parallel with the use of a low-cost drone to assess its efficiency in automatic counting of spaced canopy plants and joints. The red, green and blue bands captured by the Phantom 4 Advanced were used, and the red band with linear enhancement for the cut option, to facilitate the distinction of the orchard and the rest of the targets in the image and to obtain a better result in the detection of fruit trees. The flight was performed at a height of 80 meters with an overlap between bands of 70% and in the same range of 80%. As a result, 97.98% of fruit trees were detected in plants with well-spaced crowns and 88.52% were identified in plants with crowns together. The numbers of false positives found were small for all situations tested, these false positives being weeds. It is concluded that the technique is efficient for counting plants with fair and spaced crowns, and detection can be improved when there is a good contrast between what you want to detect and the targets that are not of interest.