Proceedings

Find matching any: Reset
Reich, R
Webber, H
Rains, G
Wang, Y
Add filter to result:
Authors
Naser, M.A
Khosla, R
Haley, S
Reich, R
Longchamps, L
Moragues, M
Buchleiter, G.W
McMaster, G.S
Naser, M.A
Khosla, R
Reich, R
Haley, S
Longchamps, L
Moragues, M
Buchleiter, G.W
McMaster, G.S
Mzuku, M
Khosla, R
Reich, R
http://icons.paqinteractive.com/16x16/ac, G
Smith, F
MacDonald, L
Webber, H
Longchamps, L
Khosla, R
Reich, R
Wang, Y
Balmos, A
Krogmeier, J
Buckmaster, D
Krogmeier, J
Buckmaster, D
Ault, A
Wang, Y
Zhang, Y
Layton, A
Noel, S
Balmos, A
Byers, C
Virk, S
Meena, R.K
Rains, G
Topics
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Spatial Variability in Crop, Soil and Natural Resources
Precision Agriculture and Climate Change
Big Data, Data Mining and Deep Learning
Profitability and Success Stories in Precision Agriculture
Drone Spraying
Type
Poster
Oral
Year
2012
2010
2016
2018
2024
Home » Authors » Results

Authors

Filter results8 paper(s) found.

1. Spatial Variability Of Measured Soil Properties Across Site- Specific Management Zones

The spatial variation of productivity across farm fields can be classified by delineating site-specific management zones. Since productivity is influenced by soil characteristics, the spatial pattern of productivity could be caused by a corresponding variation in certain soil properties. Determining the source of variation in productivity can help achieve more effective site-specific management, the objectives of this study were (i) to characterize the spatial variability of soil physical properties... M. Mzuku, R. Khosla, R. Reich, G. Http://icons.paqinteractive.com/16x16/ac, F. Smith, L. Macdonald

2. Can Active Sensor Based NDVI Consistently Classify Wheat Genotypes?

ABSTRACT ... M.A. Naser, R. khosla, S. Haley, R. Reich, L. Longchamps, M. Moragues, G.W. buchleiter, G.S. Mcmaster

3. Variation in Nitrogen Use Efficiency for Multiple Wheat Genotypes across Dryland and Irrigated Cropping Systems

ABSTRACT ... M.A. Naser, R. Khosla, R. Reich, S. Haley, L. longchamps, M. Moragues, G.W. buchleiter, G.S. Mcmaster

4. Understanding Complex Soil Variability: the Application of Archaeological Knowledge to Precision Agriculture Systems in the UK.

As higher resolution datasets have become more available and more accessible within commercial agriculture, there has been an increasing expectation that more data will bring more answers to questions surrounding soil, crop and yield variability. When this does not happen, trust and confidence in data can be lost, affecting the uptake and use of precision agriculture. This research presents a novel approach for understanding complex soil variability at a variety of different scales.... H. Webber

5. Climate Smart Precision Nitrogen Management

Climate Smart Agriculture (CSA) aims at improving farm productivity and profitability in a sustainable way while building resilience to climate change and mitigating the impacts of agriculture on greenhouse gas emissions. The idea behind this concept is that informed management decision can help achieve these goals. In that matter, Precision Agriculture goes hand-in-hand with CSA. The Colorado State University Laboratory of Precision Agriculture (CSU-PA) is conducting research on CSA practices... L. Longchamps, R. Khosla, R. Reich

6. Data-Driven Agricultural Machinery Activity Anomaly Detection and Classification

In modern agriculture, machinery has become the one of the necessities in providing safe, effective and economical farming operations and logistics. In a typical farming operation, different machines perform different tasks, and sometimes are used together for collaborative work. In such cases, different machines are associated with representative activity patterns, for example, in a harvest scenario, combines move through a field following regular swaths while grain carts follow irregular paths... Y. Wang, A. Balmos, J. Krogmeier, D. Buckmaster

7. Use Cases for Real Time Data in Agriculture

Agricultural data of many types (yield, weather, soil moisture, field operations, topography, etc.) comes in varied geospatial aggregation levels and time increments. For much of this data, consumption and utilization is not time sensitive. For other data elements, time is of the essence. We hypothesize that better quality data (for those later analyses) will also follow from real-time presentation and application of data for it is during the time that data is being collected that errors can be... J. Krogmeier, D. Buckmaster, A. Ault, Y. Wang, Y. Zhang, A. Layton, S. Noel, A. Balmos

8. Spray Deposition Characterization of Uniform and Variable-rate Applications with Spray Drones

The use of unmanned aerial application systems (also known as spray drones) has seen rapidly increasing interest in recent years due to their potential to allow for timely application of pesticides and being able to apply in areas inaccessible to ground application sprayers. Newer spray drone models’ have improved application systems such as rotary atomizers for creating spray droplets and capabilities such as variable-rate (VR) application for site-specific pesticide applications. An investigation... C. Byers, S. Virk, R.K. Meena, G. Rains