Tag Archives: UAV

644-653 M. Änäkkälä, A. Lehtilä, P.S.A. Mäkelä and A. Lajunen
Application of UAV multispectral imaging for determining the characteristics of maize vegetation
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Application of UAV multispectral imaging for determining the characteristics of maize vegetation

M. Änäkkälä¹*, A. Lehtilä²³, P.S.A. Mäkelä⁴ and A. Lajunen¹

¹University of Helsinki, Faculty of Agriculture and Forestry, Department of Agricultural Sciences, Koetilantie 5, FI00790 Helsinki, Finland
²Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI00790 Helsinki, Finland
³University of Helsinki, Helsinki Institute of Sustainability Sciences (HELSUS), Yliopistonkatu 4, FI00100 Helsinki, Finland
⁴University of Helsinki, Faculty of Agriculture and Forestry, Department of Agricultural Sciences, Latokartanonkaari 5, FI00790 Helsinki, Finland
*Correspondence: mikael.anakkala@helsinki.fi

Abstract:

Interest in forage maize (Zea mays L.) cultivation for livestock feed has grown in northern conditions. In addition, it is important to develop methods and tools to monitor crop development and other characteristics of the crop. For these purposes UAVs are very efficient and versatile tools. UAVs can be equipped with a variety of sensors like lidar or different types of cameras. Several studies have been conducted where data collected by UAVs are used to estimate different crop properties like yield and biomass. In this research, a forage maize field experiment was studied to examine how well the aerial multispectral data correlated with the different properties of the vegetation. The field test site is located in Helsinki, Finland. A multispectral camera (MicaSense Rededge 3) was used to take images from five spectral bands (Red, Green, Blue, Rededge and NIR). All the images were processed with Pix4D software to generate orthomosaic images. Several vegetation indices were calculated from the five spectral bands. During the growing season, crop height, chlorophyll content, leaf area index (LAI), fresh and dry matter biomass were measured from the vegetation. From the five spectral bands, Rededge had the highest correlation with fresh biomass (R2 = 0.273). The highest correlation for a vegetation index was found between NDRE and chlorophyll content (R2 = 0.809). A multiple linear regression (MLR) model using selected spectral bands and vegetation indices as inputs showed high correlations with the field measurements.

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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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784-793 K. Křížová, J. Haberle, M. Kroulík, J. Kumhálová and J. Lukáš
Assessment of soil electrical conductivity using remotely sensed thermal data
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Assessment of soil electrical conductivity using remotely sensed thermal data

K. Křížová¹²*, J. Haberle³, M. Kroulík¹, J. Kumhálová⁴ and J. Lukáš²

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ16500 Prague, Czech Republic
²Crop Research Institute, Division of Crop Protection and Plant Health, Drnovská 507/73, CZ 16106 Prague, Czech Republic
³Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, CZ16106 Prague, Czech Republic
⁴Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ16500 Prague, Czech Republic
*Correspondence: krizovak@tf.czu.cz

Abstract:

Detection of heterogeneity (crop, soil, etc.) gained a lot of importance in the field of site-specific farming in recent years and became possible to be measured by different sensors. The thermal spectrum of electromagnetic radiation has a great potential today and experiments focused on describing a relation between canopy temperature and various vegetation characteristics are conducted. This paper was aimed to examine the relation between canopy temperature and electrical conductivity as one of staple soil characteristics. The related experiment was undertaken in Sojovice, Czech Republic, within an agricultural plot where winter wheat was grown in 2017 growing season. The examined plot was composed of three sub plots and 35 control points were selected within this area which the data were related to. A canopy was sensed by UAV (eBee carrying thermoMAP (FLIR TAU2) camera). Soil conductivity data were collected by terrestrial sampling using EM38-MK2 Ground Conductivity Meter in 1 m depth and 2 m sampling point distance. This dataset was later interpolated using the kriging method. The correlation analysis results showed a strong negative correlation between conductivity and thermal data (-0.82; p < 0.001). When comparing conductivity with NDVI representing the aboveground biomass, there was an opposite trend but also strong result (0.86; p < 0.001). Correlation coefficient of thermal data and NDVI comparison was -0.86; (p < 0.001). These preliminary results have a potential for further research in terms of soil characteristics studies.

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