Proceedings
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| Filter results7 paper(s) found. |
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1. Economic and Environmental Impacts in Sugarcane Production to Meet the Brazilian Ethanol Demands by 2030: The Role of Precision AgricultureThe agreement signed at COP-21 reaffirms the vital compromise of Brazil with sugarcane and ethanol production. To meet the established targets, the ethanol production should be 54 billion liters in 2030. From the agronomic standpoint, two alternatives are possible; increase the planted area and/or agricultural yield. The present study aimed to evaluate the economic and environmental impacts in sugarcane production meeting the established targets in São Paulo state. In this context, were... G.M. Sanches, T.F. Cardoso, M.F. Chagas, A.C. Luciano, D.G. Duft, P.S. Magalhães, H.C. Franco, A. Bonomi |
2. Potential of Apparent Soil Electrical Conductivity to Describe Soil Spatial Variability in Brazilian Sugarcane FieldsThe soil apparent electrical conductivity (ECa) has been highlighted in the literature as a tool with high potential to map the soil fertility of fields. However, sugarcane fields still lack results that show the applicability of this information to define the soil spatial variability and its fertility conditions. The objective of the present paper was to provide a comprehensive assessment of the relationship between ECa, evaluated by electromagnetic induction (EMI) sensor, and the spatial variability... G.M. Sanches, P.S. Magalhães, H.C. Franco, A.Z. Remacre |
3. A Comparative Study of Field-Wide Estimation of Soil Moisture Using Compressive SensingIn precision agriculture, monitoring of soil moisture plays an essential role in correct decision making. In practice, regular mesh installation, or large random deployment of moisture sensors over a large field is not possible due to cost and maintenance prohibitions. Consequently, direct measurement of moisture is possible at only a few points in the field. A value for the moisture may then be estimated for the remaining areas using a variety of algorithms. It is shown that although... H. Pourshamsaei, A. Nobakhti |
4. Optimal Sensor Placement for Field-Wide Estimation of Soil MoistureSoil moisture is one of the most important parameters in precision agriculture. While techniques such as remote sensing seems appropriate for moisture monitoring over large areas, they generally do not offer sufficiently fine resolution for precision work, and there are time restrictions on when the data is available. Moreover, while it is possible to get high resolution-on demand data, but the costs are often prohibitive for most developing countries. Direct ground level measurement... H. Pourshamsaei, A. Nobakhti |
5. Evaluation of Nitrogen Recommendation Tools for Winter Wheat in NebraskaAttaining both high yield and high nitrogen (N) use efficiency (NUE) simultaneously remains a current research challenge in crop production. Digital ag technologies for site-specific N management have been demonstrated to improve NUE. This is due to the ability of digital technologies to account for the spatial and temporal distribution of crop N demand and available soil N in the field which varies greatly according to... J. Cesario pereira pinto, L. Thompson, N. Mueller, T. Mieno, G. Balboa, L. Puntel |
6. Design of an Autonomous Ag Platform Capable of Field Scale Data Collection in Support of Artificial IntelligenceThe Pivot+ Array is intended to serve as an innovative, multi-user research platform dedicated to the autonomous monitoring, analysis, and manipulation of crops and inputs at the plant scale, covering extensive areas. It will effectively address many constraints that have historically limited large-scale agricultural sensor and robotic research. This achievement will be made possible by augmenting the well-established center pivot technology, known for its autonomy, with robust power infrastructure,... S. Jha, J. Krogmeier, D. Buckmaster, D.J. Love, R.H. Grant, M. Crawford, C. Brinton, C. Wang, D. Cappelleri, A. Balmos |
7. Spectral Imaging Deep Learning Mapper for Precision AgricultureWith the growing variety of RGB cameras, spectral sensors, and platforms like field robots or unmanned aerial vehicles (UAV) in precision agriculture, there is a demand for straightforward utilization of collected field data. In recent years, deep learning has gained significant attention and delivered impressive results in the realm of computer vision tasks, such as semantic segmentation. These models have also found extensive applications in research related to precision agriculture and spectral... L. Thomas, B. Jakimow, A. Janz, P. Hostert, A. Lajunen |