Proceedings
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| Filter results12 paper(s) found. |
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1. Changes Of Data Sampling Procedure To Avoid Energy And Data Losses During Microclimates Monitoring With Wireless Sensor Networks... J.C. Benavente, C.E. Cugnasca, M.F. Barros, H.P. Santos, G. Http://icons.paqinteractive.com/16x16/ac |
2. Assessment Of Physiological Effects Of Fungicides In WheatThe use of fungicides is one of the most widespread methods implemented in intensive crop production focused in solving phytosanitary problems. The use of fungicides belonging to groups such as strobilurins has been associated with positive physiological effects such as increased tolerance against abiotic stresses, changes in plant growth regulator activities and delayed leaf senescence. The use of thermography is a non- destructive method which permits to distinguish physiological changes caused... C. Berdugo, U. Steiner, E. Oerke, H. Dehne |
3. Estimating Soil Productivity And Energy Efficiency Using Websoil Survey, Soil Productivity Index Calculator, And Biofuel Energy Systems SimulatorSoils have varying production capacities for a specific plant or sequence of plants under defined management strategies. The production capacity or “productivity” can be quantified as a mathematical function of a soils ability to sufficiently sustain plant growth... K.D. Reitsma, T.E. Schumacher |
4. Comparison of Active and Passive Spectral Sensors in Discriminating Biomass Parameters and Nitrogen Status in Wheat CultivarsSeveral sensor systems are available for ground-based remote sensing in crops. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This study was aimed to compare active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors (Crop Circle, GreenSeeker, and an active flash sensor (AFS)) were tested for their ability to assess six destructively... B. Mistele, U. Schmidhalter, K. Erdle |
5. Spatial and Temporal Variability of Corn Grain Yield as a Function of Soil Parameters, and Climate FactorsEffective site-specific management requires an understanding the influence of soil and weather on yield variability. Our objective was to examine the influence of soil, precipitation, and temperature on spatial and temporal corn grain yield variability. The study site (10 by 250 -m in size) was located in Jaboticabal, São Paulo State, on a Rhodic Hapludox. Corn yield (planted with 0.9-m spacing) was measured... T. Mueller, J. Corá, A. Castrignanò, M. Rodrigues, E. Rienzi |
6. On-The-Go pH Sensor: An Evaluation in a Kentucky FieldA commercially available on-the-go soil pH sensor measures and maps subsurface soil pH at high spatial intensities across managed landscapes. The overall purpose of this project was to evaluate the potential for this sensor to be used in agricultural fields. The specific goals were to determine and evaluate 1) the accuracy with which this instrument can be calibrated, 2) the geospatial structure of soil pH measurements,... T. Mueller, E. Gianello, B. Mijatovic, E. Rienzi, M. Rodrigues |
7. Spectral High-Throughput Assessments Of Phenotypic Differences In Spike Development, Biomass And Nitrogen Partitioning During Grain Filling Of Wheat Under High Yielding Western European ConditionsSingle plant traits such as green biomass, spike dry weight, biomass and nitrogen (N) transfer to grains are important traits for final grain yield. However, methods to assess these traits are laborious and expensive. Spectral reflectance measurements allow researchers to assess cultivar differences of yield-related plant traits and translocation parameters that are affected by different genetic material and varying amounts of available N. In a field experiment, six high-yielding wheat cultivars... U. Schmidhalter, K. Erdle |
8. Design, Error Characterization And Testing Of A System To Measure Locations Of Fruits In Tree CanopiesMapping the variability of fruit size and quality within tree canopies in commercial orchards is an important tool for implementing precision horticulture. To do so at a reasonably fast rate requires localization technologies that offer sufficient speed and accuracy, at a range long enough to cover entire trees – or several trees at a time. Existing approaches for measuring fruit locations include: manual (centimeter accuracy and measurement time in the order of minutes per... S.G. Vougioukas, F.J. Jimenez, F. Khosro anjom, R. Elkins, C. Ingels, R. Arikapudi |
9. Optical Sensors To Predict Nitrogen Demand By SugarcaneThe low effectiveness of nitrogen (N) from fertilizer is a substantial concern in worldwide which has been threatening the sustainability of sugarcane production. The increment of nitrogen use efficiency (NUE) by sugarcane genotypes associated to the best practices of fertilizer management and nutritional diagnosis methods have higher potential to reduce environment impacts of nitrogen fertilization. Due to the difficult to determine N status in soil test as well as there is not... O.T. Kolln, G.M. Sanches, J. Rossi neto, S.G. Castro, E. Mariano, R. Otto, R. Inamasu, P.S. Magalhães, O.A. Braunbeck, H.C. Franco |
10. Barriers to Adoption of Smart Farming Technologies in GermanyThe number of smart farming technologies available on the market is growing rapidly. Recent surveys show that despite extensive research efforts and media coverage, adoption of smart farming technologies is still lower than expected in Germany. Media analysis, a multi stakeholder workshop, and the Adoption and Diffusion Outcome Prediction Tool (ADOPT) (Kuehne et al. 2017) were applied to analyze the underlying adoption barriers that explain the low to moderate adoption levels of smart farming... M. Gandorfer, S. Schleicher, K. Erdle |
11. A Growth Stage Centric Approach to Field Scale Corn Yield Estimation by Leveraging Machine Learning Methods from Multimodal DataField scale yield estimation is labor-intensive, typically limited to a few samples in a given field, and often happens too late to inform any in-season agronomic treatments. In this study, we used meteorological data including growing degree days (GDD), photosynthetic active radiation (PAR), and rolling average of rainfall combined with hybrid relative maturity, organic matter, and weekly growth stage information from three small-plot research locations... L. Waltz, S. Katari, S. Khanal, T. Dill, C. Porter, O. Ortez, L. Lindsey, A. Nandi |
12. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and VisionAdvancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi |