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| Filter results15 paper(s) found. |
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1. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 DataIn recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtained... F.R. Pereira, A.A. Dos Reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães |
2. Nitrogen Status Prediction on Pasture Fields Can Be Reached Using Visible Light UAV Data Combined with Sentinel-2 ImageryPasture fields under integrated crop-livestock system usually receive low or no nitrogen fertilization rates, since the expectation is that nitrogen demand will be provided by the soybean remaining straw cropped previously. However, keeping nitrogen at suitable levels in the entire field is the key to achieving sustainability in agricultural production systems. In this sense, remote sensing technologies play an essential role in nitrogen monitoring in pastures and crops. With the launch of the... F.R. Pereira, J.P. Lima, R.G. Freitas, A.A. Dos Reis, L.R. Amaral, G.K. Figueiredo, R.A. Lamparelli, J.C. Pereira, P.S. Magalhães |
3. Delineation of Yield Zones Using Optical and Radar Remote SensingIdentifying yield zones in agricultural areas is essential for efficient resource allocation, operational optimization, and decision-making. While optical remote sensing is widely used in precision agriculture, the interest in radar remote sensing data, notably from the Sentinel-1 Synthetic Aperture Radar (SAR), has increased due to its operation in the C-band frequency, capturing data through cloud cover and the availability of free data. The main objective of this study was to evaluate whether... I.A. Da Cunha, H. Oldoni, D.D. Melo, L.R. Amaral |
4. Yield Potential Zones and Their Relationship with Soil Taxonomic Classes and Management ZonesThe use of management zones (MZ) to subdivide agricultural areas based on the variability of yield potential and production factors is increasingly being explored by scientific research and demanded by farmers. However, there is still much uncertainty about which layers of information and procedures should be adopted for this purpose. Thus, our goal was to demonstrate whether simplistic approaches to creating MZ can satisfactorily address the variability of yield potential and soil classes. For... L.R. Amaral, H. Oldoni, D.D. Melo, N.A. Rosin, M.R. Alves, J.M. Demattê |
5. Hierarchical Zoning: Targeted Sampling for Soil Attribute MappingThe mapping of soil attributes for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is done through soil sampling in a regular grid, which generally yields good results when done in denser grids. However, due to the high costs associated with sampling and analysis, sparser grids have been adopted, which has not produced good prediction results. Some studies with directed sampling points to obtain more accurate soil maps have been adopted to address... D.D. Melo, I.A. Da Cunha, T.L. Brasco, H. Oldoni, L.R. Amaral |
6. Sampling-based on Plant Vigor Zones As a Strategy for Creating Soil Attribute MapsMapping agronomically relevant soil properties for fertilizer recommendation remains challenging in precision agriculture. Traditionally, this mapping is conducted through soil sampling on a regular grid basis, where points are equally spaced primarily to ensure spatial coverage. However, directing soil sampling points based on plant vigor may be more efficient in capturing soil variability that directly affects plant development. Several commercial platforms offer solutions for defining management... D.D. Melo, T.L. Brasco, I.A. Da Cunha, S.G. Castro, L.R. Amaral |
7. Correcting LiDAR-based Plant Height Estimation Errors in Dense Cotton CanopiesCotton is a perennial plant grown as an annual crop and, if not properly managed, excessive vegetative growth may reduce yield, making the use of plant growth regulators (PGR) essential. In precision agriculture, spatial representation of PGR requirements depends on plant height measurements, which are typically labor-intensive. LiDAR sensors mounted on drones have been widely used to estimate plant height. However, under certain conditions, cotton plants can become highly vigorous, resulting... P. Zolin, L. Peranzoni Deponti, L. Bastos, L.R. Amaral |
8. Economic Assessment of Soil Sampling Densities for Variable-Rate Fertilizer PrescriptionSoil sampling and laboratory analysis represent a substantial share of operational costs in precision agriculture, making sampling density a crucial management parameter. Sampling density defines the resolution at which soil spatial variability is captured and directly influences the accuracy of spatial interpolation and fertilizer prescription maps. Due to high operational costs, reduced sampling densities are commonly adopted in practice, despite their known effects on map quality. This study... L. Delgado Bejarano, B. , A. Novaes Da Silva, L.R. Amaral |
9. Sensor-based Optimal Delineation of Management Zones for Plant and Soil IntegrationStrategies for mapping management zones (MZs) use proximal and orbital sensors to optimize use and promote sustainable development. Proximal sensors can infer, among other variables, apparent electrical conductivity (ECa), while orbital sensors provide synthetic soil images (SYSI), elevation, and different color-composition images of soil and plant canopies. However, the literature does not provide clear evidence on the efficiency of these measurements, individually and jointly, in reducing the... A.V. Hereman, J.V. Pozzuto, L.R. Amaral |
10. Effect of Post-processing on the Performance of Clustering Algorithms for Delineating Management Zones for Precision Soil SamplingSoil nutrient variability directly influences crop yield; therefore, site-specific management requires maps that can depict variability within the fields. Here, management zone (MZ) approaches have been used, typically derived from clustering analyses applied to low-cost environmental variables. There is still no consensus on zone delineation strategies that maximize the reduction of soil variability, nor on the relative performance of clustering algorithms. Moreover, post-processing of... D.D. Melo, L.R. Amaral, L. Bastos, S. Virk |
11. Comparing Traditional Methods and Digital Platforms for Delineating Management Zones: A Study of Efficiency and AccuracyDigital platforms have emerged as user-friendly tools to support management zone delineation and field monitoring in precision agriculture. However, the algorithms and methods embedded in these platforms may overlook agronomic and operational constraints, limiting their effectiveness in decision-making. This study evaluated the performance of three commercial digital platforms for management zone delineation and compared them with a reference protocol and an... T. Costa Barboza, H. Oldoni, F.D. Inácio, L.R. Amaral, A. Felipe Dos Santos |
12. Remote Sensing for Identification of Soil Texture Variability in Precision AgricultureUnderstanding spatial variability of soil texture is essential for site-specific management in Precision Agriculture. Traditional approaches rely on intensive soil sampling and apparent electrical conductivity (ECa) surveys, which provide high-quality information but may be costly and difficult to scale. Satellite remote sensing offers a promising alternative by enabling indirect estimation of soil properties through spectral responses. This study evaluated the potential of Sentinel-2 spectral... T. , L.R. Amaral |
13. RAVI: A QGIS plugin for satellite remote sensing applications of Vegetation Indices and SAR data in Precision AgricultureRemote Sensing (RS) plays a fundamental role in Precision Agriculture (PA), particularly through the use of satellite imagery to identify spatial variability within the fields. Compared to traditional methods for detecting field variability, such as soil sampling, yield mapping, and proximal sensors, RS offers advantages in reduced operational costs, lower labor demands, and greater spatial coverage. Analyzing vegetation indices (VIs) over time allows to track crop phenological development, identify... |
14. Optimization of Flight Parameters for High-Throughput Phenotyping of Guineagrass Using UASA fenotipagem de alto rendimento utilizando sistemas aéreos não tripulados (UAS) tornou-se uma ferramenta estratégica na agricultura de precisão aplicada a pastagens, permitindo a coleta rápida e não destrutiva de dados em larga escala. No entanto, a definição adequada dos parâmetros de voo, especialmente a distância de amostragem do solo (GSD) e a sobreposição de imagens, permanece um desafio, visto que configurações... |
15. Comparison of Real and Simulated GSD in the Estimation of Canopy Height in Maize Using RPAHigh-throughput phenotyping using remotely piloted aircraft (RPA) has become a strategic tool in precision agriculture, enabling rapid and non-destructive estimation of crop traits such as canopy height. Ground Sampling Distance (GSD) is a critical flight parameter in this process. Resolutions derived from smaller GSD values improve accuracy but increase flight time, image volume, and processing cost, whereas the opposite reduces these demands at a possible cost to accuracy. Simulating coarser... C. Ragalzi, M.J. Lima, L. Felipe, N. Guimarães, M.F. Santos |