992 resultados para Precision Agriculture


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Na aplicação de produtos fitossanitários, a utilização de equipamentos que controlam automaticamente as seções da barra e a pulverização já é realidade; entretanto, ainda não há resultados que demonstrem a sua eficácia. Por esse motivo, este trabalho teve por finalidade desenvolver uma metodologia para a avaliação de um equipamento que controla automaticamente as seções e a pulverização. Avaliou-se um controlador automático de seções e pulverização de mercado, e, para tanto, foram utilizados três níveis de acurácia do sinal de GPS (algoritmo interno, SBAS e RTK), três ângulos para a simulação de entrada e saída da barra de pulverização em relação à borda do talhão (0; 45 e 60º ) e três velocidades de trabalho (1,66; 5,00 e 8,33 m s-1). A metodologia proposta possibilitou a determinação dos tempos e distâncias de abertura e fechamento das seções. Os coeficientes de variação para os tempos e distâncias de abertura e fechamento das seções indicaram uma variação considerável. Houve interações significativas em função do tipo de sinal de GPS. A configuração recomendada pelo fabricante e adotada para a avaliação do controlador automático de seções e pulverização não atende a todas as situações simuladas.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The CERES-Maize model was used to estimate the spatial variability in corn (Zea mays L.) yield for 1995 and 1996 using data measured on soil profiles located on a 30.5 m grid within a 3.9 ha field in Michigan. The model was calibrated for one grid profile for the 1995 and then used to simulate corn yield for all grid points for the 2 yrs. For the calibration for 1995, the model predicted corn yield within 2%. For 1995, the model predicted yield variability very well (r(2) = 0.85), producing similar yield maps with differences generally within +/- 300 kg ha(-1). For 1996, the model predicted low grain yields (1167 kg ha(-1)) compared with measured (8928 kg ha(-1)) because the model does not account for horizontal water movement within the landscape or water contributions from a water table. Under nonlimiting water conditions, the model performed well (average of 8717 vs. 8948 kg ha(-1)) but under-estimated the measured yield variability.

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The objective of this work was to model and diagnose the spatial variability of soil load support capacity (SLSC) in sugar cane crop fields, as well as to evaluate the management impact on São Paulo State soil structure. The investigated variables were: pressure preconsolidation (sigma(p)), apparent cohesion () and internal friction angle (). The conclusions from the results were that the models and spatial dependence maps constitute important tools in the prediction and location of the mechanical internal strength of soils cultivated with sugar cane. They will help future soil management decisions so that soil structure sustainability will not be compromised.

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The aim of this study was to develop and evaluate a variable dose rate application of herbicides using an online electronic control based system with optical sensors for weed detection in forested areas. The proposed concept was to apply a basic dose on 100% of the area (aiming to control small weeds) and to apply a complementary patch-spraying dose only on areas with higher weed infestation. For that purpose, a conventional spray boom was adjusted to apply 40% of the herbicide dose on the full area and the optical sensors were used to control the application of the complementary dose (60%) only on areas with higher infestation. The results showed that the system performed adequately. Field applications presented herbicide savings around 20 to 30%, with a similar weed control performance as compared to the full dose application on 100% of the area.

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A harmful and limiting factor regarding culture productivity is soil compaction, a parameter that can be quantified by the resistance of the soil to penetration and highly influenced by water content. These variables present spatial and temporary variability, characteristics which can be determined by the geostatistical technique. In the light of the above, the present work had as its objective the study of spatial variability of soil resistance to penetration (RP) and water content in the soil (U) in soy culture. The RP values at depths of 0,00-0,10; 0,11-0,20 and 0,21-0,30 m varied from 2,9 to 4,28 MPa and are considered harmful to the root development of legumes, although they have not influenced soy productivity which was 3887 kg ha(-1). The medium water content of the soil was between 0,210 and 0,213 kg kg(-1) for the three depths studied. The resistance of the soil to penetration, expressed through semivariograms, presented spatial dependence at all depths, being adjusted to the spherical model at depths of 0,00-0,10m and exponential at depths of 0,110,20 and 0,21-0,30 m. The spatial variability for all studied layers presented a range of about 20m. The water content in the soil did not present spatial dependence for the depths, presenting randomized distribution.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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In this work an image pre-processing module has been developed to extract quantitative information from plantation images with various degrees of infestation. Four filters comprise this module: the first one acts on smoothness of the image, the second one removes image background enhancing plants leaves, the third filter removes isolated dots not removed by the previous filter, and the fourth one is used to highlight leaves' edges. At first the filters were tested with MATLAB, for a quick visual feedback of the filters' behavior. Then the filters were implemented in the C programming language. At last, the module as been coded in VHDL for the implementation on a Stratix II family FPGA. Tests were run and the results are shown in this paper. © 2008 Springer-Verlag Berlin Heidelberg.

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The operational details of the apparent electrical conductivity (ECa) sensor manufactured by Veris Technologies have been extensively documented in literature reports, but the geographical distribution of these research studies indicate a strong regional concentration in the US Mid-west and Southern states. The agricultural lands of these states diverge significantly to the soil conditions and water regime of irrigated land in the US South-western states such as Arizona where there is no previous research reports of the use of this particular sensor. The objectives of the present study were to analyze the performance of this sensor under the conditions of typical soils in irrigated farms of Central Arizona. We tested under static conditions the performance of the sensor on three soils of contrasting texture. Observations were collected as time series data as soil moisture changed from saturation to permanent wilting point. Observations were repeated at the hours of lowest and highest temperatures. In addition, this study included soil penetration resistance and salinity determinations. Preliminary results indicate that soil temperature of the upper layer caused the most dynamic change in the sensor output. The ECa curves of the three soil textures tested had well defined distinctive characteristics. Final multivariate analysis is pending.

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The objective of this work was to study spatial variability of some chemical soil attributes and lettuce production (total shoot fresh matter mass - MF; commercial shoot fresh matter mass - MFC; commercial shoot dry matter mass - MCS; and head commercial diameter - DCC) offering subsidies to the protected environment mapping in nutrients management areas in lettuce culture aiming for a higher productivity with application of fertilizers. The experiment was conducted in a protective environment (greenhouse) with lettuce irrigated by drip irrigation and sampling grid with 152 points. The special dependence analysis, determined by the variogram, was obtained with the aid of the GS+ Program. Considering the need for crop nutrients through the map obtained for element P (phosphorus) it was possible to establish two distinct areas for the application of this element in plantation fertilization. Through the lettuce yield maps obtained with MFC and DCC attributes was difficult to establish distinct areas for its management with data observed in only one crop cycle. Krigagem has proved useful for mapping the attributes studied.

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Mapping the plant nutritional condition allows viewing different regions in a cropping area, providing the producers with different criteria to use foliar and soil fertilization. The aim of this study was to evaluate the spatial variability of the nutritional condition of canephora coffee (Coffea canephora Pierre ex Froehner) regarding the site specific management of foliar and soil fertilization. In a one hectare area 60 georeferenced points were sampled at irregular intervals. There were five plants in each sampled point; two pairs of leaves were removed from the lateral branches (3 rd and 4 th pairs from extremity to the basis) in the cardinal points of each plant, counting up 40 leaves per point. The foliar samples were chemically analyzed for the following nutrients: N, P, K, Ca, Mg, S, B, Cu and Zn. The same pattern of spatial dependence was presented with adjustment for K and B. Except for N and P, which presented random distribution, the other nutrients presented mild to severe spatial dependence justifying the geostatistical data analysis for making maps for differential and located, foliar and soil fertilizer application in coffee crop.

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The spatial variability of several soil attributes (bulk density, penetration resistance, water content, organic matter content and pH) as well as soybean yield have been assessed during the 2007/08 growing season, in Selviria (MS) in a Hapludox (Typic Acrustox), under no tillage. The objectives were to assess the spatial variability of soil and plant parameters at the small plot scale and to select the best soil attribute explaining most the variability of agricultural productivity. Soil and plant were sampled on a grid with 121 points within a plot of 25,600 m 2 in area and slope of 0.025 mm -1 slope. Medium and low coefficients of variation were obtained for most of the studied soil attributes as expected, due to the homogenizing effect of the no-till system on the soil physical environment. From the standpoint of linear regression and spatial pattern of variability, productivity of soybeans could be explained according to the hydrogen potential (pH). Results are discussed taken into account that the soybean crop in no-tillage is widely used in crop-livestock integration on the national scene.

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A current trend in the agricultural area is the development of mobile robots and autonomous vehicles for precision agriculture (PA). One of the major challenges in the design of these robots is the development of the electronic architecture for the control of the devices. In a joint project among research institutions and a private company in Brazil a multifunctional robotic platform for information acquisition in PA is being designed. This platform has as main characteristics four-wheel propulsion and independent steering, adjustable width, span of 1,80m in height, diesel engine, hydraulic system, and a CAN-based networked control system (NCS). This paper presents a NCS solution for the platform guidance by the four-wheel hydraulic steering distributed control. The control strategy, centered on the robot manipulators control theory, is based on the difference between the desired and actual position and considering the angular speed of the wheels. The results demonstrate that the NCS was simple and efficient, providing suitable steering performance for the platform guidance. Even though the simplicity of the NCS solution developed, it also overcame some verified control challenges in the robot guidance system design such as the hydraulic system delay, nonlinearities in the steering actuators, and inertia in the steering system due the friction of different terrains. Copyright © 2012 Eduardo Pacincia Godoy et al.

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Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

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The precision agriculture technologies such as the spatial variability of soil attributes have been widely studied mostly with sugarcane. Among these technologies have been recently highlighted the use of the vegetation index derived from remote sensing products, such as powerful tools indicating the development of vegetation. This study aimed to analyze the spatial variability of clay content, pH and phosphorus in an Oxisol in an area with sugarcane production, and correlate with the Normalized Difference Vegetation Index (NDVI). The georeferenced grid was created for the soil properties (clay, phosphorus and pH) and generated the maps of spatial variability. For these same sites were calculated the NDVI, in addition to mapping of this ratio, the evaluation of the spatial correlation between this and other studied properties. The clay and phosphorus content showed positive spatial correlation with the NDVI, while no spatial correlation was observed between NDVI and pH. The satellite images from the sensor ETM + Landsat were used to correlate to NDVI to observe the spatial variability of the studied attributes.