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Sirvi Autor "Barbosa, B.D.S." järgi

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  • Pisipilt
    Kirje
    Analysis of flight parameters and georeferencing of images with different control points obtained by RPA
    (2019) Santos, L.M.D.; Ferraz, G.A.S.; Andrade, M.T.; Santana, L.S.; Barbosa, B.D.S.; Maciel, D.T.; Rossi, Giuseppe
    New techniques for analysing the earth's surface have been explored, such as the use of remotely piloted aircraft (RPA) to obtain aerial images. However, one of the obstacles of photogrammetry is the reliability of the scenes, because in some cases, considerable geometric errors are generated, thus necessitating adjustments. Some parameters used in these adjustments are image overlaps and control points, which generate uncertainties about the amount and arrangement of these points in an area. The aim of this study was to test the potential of a commercial RPA for monitoring and its applicability in the management of and decision-making about coffee crops with two different overlaps and to evaluate geometric errors by applying four grids of georeferenced points. The study area is located in an experimental Arabica coffee plantation measuring 0.65 ha. To capture the images, the flight altitude was standardized to a 30 m altitude from the ground, and a constant travel speed of 3 m s -1 was used. The treatments studied were two combinations of image overlap, namely, 80/80% and 70/60%. Six points were tracked through Global Navigation Satellite System (GNSS) receivers and identified with signs, followed by an RPA flight for image collection. The obtained results indicated distinct residual error rates pointing to larger errors along Cartesian axis Y, demonstrating that the point distribution directly affects the residual errors. The use of control points is necessary for image adjustments, but to optimize their application, it is necessary to consider the shape of the area to be studied and to distribute the points in a non-biased way relative to the coordinate axes. It is concluded that the lower overlap can be recommended for use in the flight plan due to the high resolution of the orthomosaic and the shorter processing time.
  • Pisipilt
    Kirje
    Coffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS images
    (2020) 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.
    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 (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.
  • Pisipilt
    Kirje
    Design and construction of a low-cost remotely piloted aircraft for precision agriculture applications
    (2019) Morerira, M.G.; Ferraz, G.A.S.; Barbosa, B.D.S.; Iwasaki, E.M.; Ferraz, P.F.P.; Damasceno, F.A.; Rossi, G.
    This study aimed to construct a low cost RPA capable of recording georeferenced images. For the construction of the prototype of a quadcopter type RPA, only essential materials were used to allow stable flight. A maximum total weight of 2 kg was stipulated, including frame weight, electronic components, motors and cameras. The aircraft was programmed using a low-cost microcontroller widely used in prototyping and automation research. An electronic circuit board is designed to facilitate the connection of the microcontroller with the other components of the design. Specific software was used for flight control. The prototype was built successfully, being able to lift stable and controllable flight. However, we still need to acquire equipment and programming components capable of enabling autonomous images and flights. The final cost of the RPA was on average $ 427.00 on average 50% lower than the values found in the Brazilian ARP market ($ 772.81 to $ 1,288.00)
  • Pisipilt
    Kirje
    RGB vegetation indices applied to grass monitoring: a qualitative analysis
    (2019) Barbosa, B.D.S.; Ferraz, G.A.S.; Gonçalves, L.M.; Marin, D.B.; Maciel, D.T.; Ferraz, P.F.P.; Rossi, G.
    In developing countries such as Brazil, research on low-cost remote sensing and computational techniques become essential for the development of precision agriculture (PA), and improving the quality of the agricultural products. Faced with the scenario of increasing production of emerald grass (Zoysia Japônica) in Brazil, and the value added the quality of this agricultural product. The objective of this work was to evaluate the performance of RGB (IV) vegetation indices in the identification of exposed soil and vegetation. The study was developed in an irrigated area of 58 ha cultivated with emerald grass at Bom Sucesso, Minas Gerais, Brazil. The images were obtained by a RGB digital camera coupled to an remotely piloted aircraft. The flight plan was setup to take overlapping images of 70% and the aircraft speed was 10 m s -1 . Six RGB Vegetation index (MGVRI, GLI, RGBVI, MPRI, VEG, ExG) were evaluated in a mosaic resulting from the images of the study area. All of the VIs evaluated were affected by the variability of lighting conditions in the area but MPRI and MGVRI were the ones that presented the best results in a qualitative evaluation regarding the discrimination of vegetation and soil.