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Lee, C
Lebeau, F
Lund, E
Lacasa, J
Lacey, R
Zebrath, B
Sassenrath, G.F
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Authors
Lund, E
Maxton, C
Kweon, G
Lee, C
Griffin, T
Dumont, B
Vancutsem, F
Destain, J
Bodson, B
Lebeau, F
Destain, M
Lan, Y
Zhang, H
Yang, C
Martin, D
Lacey, R
Huang, Y
Hoffmann, W.C
Moulton, P
Sassenrath, G.F
Mueller, T
Alarcon, V.J
Kulesza, S.E
Shoup, D
Lund, E
Maxton, C
Lund, T
Cambouris, A
Lajili, A
Chokmani , K
Perron, I
Adamchuk, V
Biswas , A
Zebrath, B
Lund, E
Lund, T
Maxton, C
Lund, E
Lund, T
Lund, E
Maxton, C.R
Maxton, C.R
Lund, T
Lund, E
Hernandez, C
Correndo, A
Lacasa, J
Magalhaes Cisdeli, P
Nocera Santiago, G.N
Ciampitti, I
Topics
Proximal Sensing in Precision Agriculture
Guidance, Auto Steer, and GPS Systems
Sensor Application in Managing In-season Crop Variability
Remote Sensing Applications in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Industry Sponsors
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Site-Specific Nutrient, Lime and Seed Management
Decision Support Systems
Type
Poster
Oral
Year
2012
2010
2016
2018
2022
2024
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Authors

Filter results12 paper(s) found.

1. The Ultimate Soil Survey in One Pass: Soil Texture, Organic Matter, pH, Elevation, Slope, and Curvature

The goal of accurately mapping soil variability preceded GPS-aided agriculture, and has been a challenging aspect of precision agriculture since its inception.  Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling.  Other studies have examined variability within government soil surveys and concluded that they have limited utility in many precision applications.  Proximal soil sensing has long been envisioned as a method... E. Lund, C. Maxton, G. Kweon

2. The Cost Of Dependence Upon GPS-enabled Navigation Technologies

The adoption of global positioning system (GPS) technology to fine-tune agricultural field operations over the last decade has been unprecedented relative to other agricultural technologies. Resultantly, as agricultural machinery size and capacity increased, field operations have become much more precise due to the synergistic relationship between farm machinery and GPS-enabled guidance technology. With increased dependence upon GPS technology, one must ask “What are the risks associated... C. Lee, T. Griffin

3. A Model For Wheat Yield Prediction Based On Real-time Monitoring Of Environmental Factors

... B. Dumont, F. Vancutsem, J. Destain, B. Bodson, F. Lebeau, M. Destain

4. Multisensor Data Fusion Of Remotely Sensed Imagery For Crop Field Mapping

  A wide variety of remote sensing data from airborne hyperspectral and multispectral images is available for site-specific management in agricultural application and production. Aerial imaging system may offer less expensive and high spatial resolution imagery with Near Infra-Red, Red, Green and Blue spectral wavebands. Hyperspectral sensor provides hundreds of spectral bands. Multisensor data fusion provides an effective paradigm for remote sensing applications by synthesizing... Y. Lan, H. Zhang, C. Yang, D. Martin, R. Lacey, Y. Huang, W.C. Hoffmann, P. Moulton

5. In-field Variability of Terrain and Soils in Southeast Kansas: Challenges for Effective Conservation

A particular challenge for crop production in southeast Kansas is the shallow topsoil, underlain with a dense, unproductive clay layer. Concerns for topsoil loss have shifted production systems to reduced tillage or conservation management practices. However, historical erosion events and continued nutrient and sediment loss still limit the productive capacity of fields. To improve crop production and further adoption of conservation practices, identification of vulnerable areas of fields was... G.F. Sassenrath, T. Mueller, V.J. Alarcon, S.E. Kulesza, D. Shoup

6. A Data Fusion Method for Yield and Soil Sensor Maps

Utilizing yield maps to their full potential has been one of the challenges in precision agriculture.  A key objective for understanding patterns of yield variation is to derive management zones, with the expectation that several years of quality yield data will delineate consistent productivity zones.  The anticipated outcome is a map that shows where soil productive potentials differ.  In spite of the widespread usage of yield monitors, commercial agriculture has found it difficult... E. Lund, C. Maxton, T. Lund

7. Use of Proximal Soil Sensing to Delineate Management Zones in a Commercial Potato Field in Prince Edward Island, Canada

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties. Such MZs can often be used for site-specific management of crop production inputs. The purpose of this study was to determine the efficiency of two proximal soil sensors for delineating MZs in an 8.1-ha commercial potato (Solanum tuberosum L.) field in Prince Edward Island (PEI), Canada. A galvanic contact resistivity sensor (Veris-3100 [Veris]) and electromagnetic induction sensors... A. Cambouris, A. Lajili, K. Chokmani , I. Perron, V. Adamchuk, A. Biswas , B. Zebrath

8. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile Sensing

Soils have a large carbon storage capacity and sequestering additional carbon in agricultural fields can reduce CO2 levels in the atmosphere, helping to mitigate climate change. Efforts are underway to incentivize agricultural producers to increase soil organic carbon (SOC) stocks in their fields using various conservation practices.  These practices and the increased SOC provide important additional benefits including improved soil health, water quality and – in some cases –... E. Lund, T. Lund, C. Maxton

9. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile Sensing

Measuring soil carbon is currently a subject of significant interest due to soil’s ability to sequester carbon and reduce atmospheric CO2. The cost of conventional soil sampling and analysis along with the number of samples required make proximal sensing an appealing option.  To properly evaluate the performance of proximal sensing of soil carbon, a detailed lab-analyzed carbon inventory is needed to serve as the ‘gold standard’ in evaluating sensor estimations.  Four... E. Lund

10. Accurately Mapping Soil Profiles: Sensor Probe Measurements at Dense Spatial Scales

Proximal sensing of soil properties has typically been accomplished using various sensor platforms deployed in a continuous sensing mode collecting data along transects, typically spaced 10-20 meters apart. This type of sensing can provide detailed maps of the X-Y soil variability and some sensors provide an indication of soil properties within the profile, however without additional investigations the profile is not delineated precisely.  Alternatively, soil sensor probes can provide detailed... T. Lund, E. Lund, C.R. Maxton

11. Using Soil Samples and Soil Sensors to Improve Soil Nutrient Estimations

Estimating soil nutrient levels, especially immobile nutrients like P and K, has been a primary activity for providers of precision agriculture services.  Soil nutrients often vary widely within fields and growers have been eager to manage them site-specifically.  There are many causes of the variability, including pedogenic factors such as soil texture, organic matter, landscape position and other factors that have resulted in an accumulation of unused nutrients in some areas of the... C.R. Maxton, T. Lund, E. Lund

12. From Scientific Literature to the End User: Democratizing Access to Data Products Through Interactive Applications

In recent years, the sustained advance in the creation of powerful programming libraries is allowing not only the creation of complex models with predictive capabilities but also revolutionizing visualization processes and the deployment of interactive applications. Some of these tools, such as Streamlit or Shiny frameworks in languages such as Python or R, allow us to create from simple applications with friendly interfaces to complex tools. These interactive digital decision dashboards allow... C. Hernandez, A. Correndo, J. Lacasa, P. Magalhaes cisdeli, G.N. Nocera santiago, I. Ciampitti