Tag Archives: drone

249-265 J.V. Aguiar, P.F.P. Ferraz, G.A.S. Ferraz, J.C. Ferreira, D. Cecchin, A. Mattia, L. Conti and G. Rossi
Remotely piloted aircraft for monitoring greenhouse gases in dairy production systems
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Remotely piloted aircraft for monitoring greenhouse gases in dairy production systems

J.V. Aguiar¹, P.F.P. Ferraz²*, G.A.S. Ferraz², J.C. Ferreira², D. Cecchin³, A. Mattia⁴, L. Conti⁴ and G. Rossi⁴

¹Federal University of Lavras (UFLA), Department of Animal Science, Faculty of Animal Science and Veterinary Medicine, BR 37200–900 Lavras, Brazil
²Federal University of Lavras (UFLA), Department of Agricultural Engineering, BR37200–900 Lavras, Brazil
³Department of Agricultural and Environmental Engineering, Fluminense Federal University (UFF), BR 24210–240 Niteroi, Brazil
⁴Department of Agriculture, Food, Environment and Forestry, University of Florence, IT 50145 Florence, Italy
*Correspondence: patricia.ponciano@ufla.br

Abstract:

The monitoring of greenhouse gas (GHG) emissions in dairy cattle facilities is essential for understanding and mitigating the environmental impact of livestock farming. Among the main gases emitted in dairy production systems, methane (CH4) and carbon dioxide (CO2) play significant roles in global warming. The objective of this research was to evaluate the spatial variability of CH4 (ppm) and CO2 (ppm) concentrations, as well as environmental variables (dry bulb temperature, tdb, °C, and relative humidity, RH, %), in a compost barn dairy production system. For gas concentration monitoring, an electrochemical sensor was used for CH4 and a non–dispersive infrared (NDIR) sensor for CO2. For the environmental variables, a Hobo® MX2301A datalogger was used, and both pieces of equipment were attached to a remotely piloted aircraft (RPA), the DJI Matrice 350. Measurements were carried out over three days, with flights conducted over the facility’s roof. The data obtained were analysed using geostatistics to characterise spatial variability of the GHG. A strong spatial dependence was observed in gas concentrations and environmental variables. The highest concentrations of CH4 (129–134.4 ppm) and CO2 (434–479 ppm) were recorded on the first day. Tdb ranged between 24.2 °C and 32 °C, while RH fluctuated between 38.8% and 68%. The use of RPA proved to be an efficient tool for GHG monitoring, allowing the identification of spatial distribution patterns. This technology provides a novel approach to measuring GHG emissions, addressing the environmental challenges of the agricultural sector.

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xxx B.L.P. Ramos, A.A. Seixas, L.M.G. Nascimento, D.L.S. Dias, J.M.S. Amorim, O.L. Lemos and M.S. Pedreira
Development of tropical grassland biomass prediction model based on UAV RGB images
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Development of tropical grassland biomass prediction model based on UAV RGB images

B.L.P. Ramos¹*, A.A. Seixas¹, L.M.G. Nascimento¹, D.L.S. Dias⁴, J.M.S. Amorim¹, O.L. Lemos³ and M.S. Pedreira²

¹University State Southwestern Bahia, Postgraduate Program in Animal Science,
BR 415, Itapetinga – BA, Brazil
²University State Southwestern Bahia, Department of Plant Science and Animal Science, Road of Good Will, km 04, Vitória da Conquista – BA, Brazil
³University State Southwestern Bahia, Department of Agricultural and Soil Engineering, Road of Good Will, km 04, Vitória da Conquista – BA, Brazil
⁴University State of Feira de Santana, Department of Biological Sciences, Av. Transnordestina, Feira de Santana – BA, Brazil
*Correspondence: agro.barbara@outlook.com

Abstract:

The objective of this study is to assess the predictive potential of indices derived from RGB images captured by a camera mounted on a remotely piloted vehicle (RPV) to estimate the fresh and dry forage yield of grasses from the Urochloa genus. The experiment was conducted between December 2021 and January 2023, involving four cultivars of the Urochloa genus (U. brizantha cv. Braúna, U. brizantha cv. Paiaguás, U. hybrid cv. Camello, and U. decumbens cv. Basilisk), with flights conducted at two heights (20 and 100 metres). The values of the Green Leaf Index (GLI) and Digital Vegetation Model (DVM) extracted were correlated with the yields of fresh (FFY), dry forage yield (DFY), dry matter content (DM), and crude protein (CP). The results showed that DVM exhibited greater efficiency in estimating DM and CP at a flight altitude of 20 m. In contrast, GLI proved more efficient in estimating FFY and DFY at 100 m altitude, suggesting the potential for combining DVM and GLI to develop predictive models. The RGB images obtained via RPV have potential for estimating forage productivity and quality, expanding the possibilities of pasture management techniques.

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1463-1471 L.M. Santos, G.A.S. Ferraz, A.V. Diotto, B.D.S. Barbosa, D.T. Maciel, M.T. Andrade, P.F.P. Ferraz and G. Rossi
Coffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS images
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Coffee crop coefficient prediction as a function of biophysical variables identified from RGB UAS images

L.M. Santos¹*, G.A.S. Ferraz¹, A.V. Diotto², B.D.S. Barbosa¹, D.T. Maciel², M.T. Andrade¹, P.F.P. Ferraz¹ and G. Rossi³

¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000 Lavras-MG, Brazil
²Federal University of Lavras, Department of Water Resources and sanitation, University Campus, BR37.200-000 Lavras, Brazil
³University of Florence, Department of Agricultural, Food, Environment and Forestry (DAGRI), Via San Bonaventura, 13, IT50145 Florence, Italy
*Correspondence: luanna_mendess@yahoo.com.br

Abstract:

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.

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1984–1992 M.G. Morerira, G.A.S. Ferraz, B.D.S. Barbosa, E.M. Iwasaki, P.F.P Ferraz, F.A. Damasceno and G. Rossi
Design and construction of a low-cost remotely piloted aircraft for precision agriculture applications
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Design and construction of a low-cost remotely piloted aircraft for precision agriculture applications

M.G. Morerira¹, G.A.S. Ferraz¹*, B.D.S. Barbosa¹, E.M. Iwasaki¹, P.F.P Ferraz¹, F.A. Damasceno² and G. Rossi³

¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000, Lavras, Brazil
²Federal University of Lavras, Department of Engineering, University Campus, BR37.200-000, Lavras, Brazil
³University of Florence, Department of Agriculture, Food, Environment and Forestry (DAGRI), Via San Bonaventura, 13, IT50145 Florence, Italy
*Correspondence: gabriel.ferraz@ufla.br

Abstract:

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)

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