Proceedings
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| Filter results6 paper(s) found. |
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1. Economics Of Precision Agriculture For Wheat And Barley Cultivation In Hamedan, Western IranPrecision agriculture can influence agricultural operation economics. In this study, minimum economical farm sizes for producing irrigated/dry wheat and barley in... M. Lak, F. Khosro anjom, J. Tatge |
2. Soil Organic Carbon Maintenance Requiremnets And Mineralizatyion Rate Constants: Site Specific CalcuationsOver the past 100 years numerous studies have been conducted with the goal of quantifying the impact of management on carbon turnover. It is difficult to conduct a mechanistic evaluation of these studies because each study was conducted under unique soil, climatic, and management conditions. Techniques for directly comparing data from unique studies are needed. This study discusses techniques for comparing data collected... D.E. Clay, G. Carlson, J. Tatge |
3. Assessment of the Information Content in Solar Reflective Satellite Measurements with Respect to Crop Growth Model State VariablesTo increase the utilization of satellite remote sensing data in precision agriculture, it is necessary to retrieve the most relevant variables from the satellite signals so that the retrievals can be directly utilized by agricultural management entities. The variables that make up the state vector description of existing crop growth models provide inherent relevance to on-farm decision making because they can be used to predict future crop status based on changing farm inputs. In this study, the... N. Levitan, B. Gross |
4. Real-Time Control of Spray Drop ApplicationElectrostatic application of spray drops provides unique opportunities to precisely control the application of pesticides due to the additional electrostatic force on the spray drops, in addition to the normally seen forces of aerodynamic drag, gravity, and inertia. In this work, we develop a computational model to predict the spray drop trajectories. The model is validated through experiments with high speed photography of spray drop trajectories, and quantification of which trajectories lead... S. Post, M. Jermy, P. Gaynor, N. Kabaliuk, A. Werner |
5. 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 |
6. 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 |