Proceedings
Authors
| Filter results5 paper(s) found. |
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1. A New Sensing System for Immediate and Direct Measurements of Soil NitrateIn-season management of nitrogen is a critical component in the drive to increase the nitrogen use efficiency of commercial crop production. Increasing nitrogen use efficiency itself has become a prominent issue due to both economic and environmental/regulatory drivers over the last decade. Solum, Inc (Mountain View, CA) has developed a new sensing technology to enable the immediate and direct measurement of soil nitrate. This allows rapid and economical soil... M. Preiner |
2. Field Moist Processing for Soil Analysis: Precision Measurement is Required for Precision ManagementIt has been well established over the last 50 years that many of the typical processes used by conventional soil analysis (such as drying and grinding the soil during preparation) can affect measured soil nutrient values. However, these processes have become conventional practice due to a lack of commercially viable methods of processing soil in its native field moist state. Solum, Inc (Mountain View, CA) has developed a process that allows routine, high throughput measurement... M. Preiner |
3. 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 |
4. 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 |
5. Establishment of Spatial Information for Soybean Cultivation Complex Through Drone Image AnalysisThis study demonstrates that time-series drone imagery can effectively monitor crop growth in large-scale soybean paddy complexes. Additionally, spatial data were constructed for each field, including geographic coordinates, parcel numbers, area, crop type, sowing date, and growth information. ... J. Park |