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Poncet, A
Uhlmann, N
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Authors
Fulton, J.P
Balkcom, K.S
Ortiz, B.V
McDonald, T.P
Pate, G.L
Virk, S.S
Poncet, A
Reisinger, S
Uhlmann, N
Hanke, R
Gerth, S
Claussen, J
Wörlein, N
Uhlmann, N
Gerth, S
Poncet, A
Bui, T
France, W
Roberts, T
Purcell, L
Kelley, J
France, W
Poncet, A
Sigdel, U
Ross, J
Tagoe, A
Koparan, C
Poncet, A
Johnson, D.M
Worthington, M
Wang, D
Topics
Engineering Technologies and Advances
Sensor Application in Managing In-season CropVariability
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Decision Support Systems
Site-Specific Nutrient, Lime and Seed Management
Scouting and Field Data collection with Unmanned Aerial Systems
Type
Oral
Poster
Year
2014
2018
2024
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1. Row-Crop Planter Requirements To Support Variable-Rate Seeding Of Maize

Current planting technology possesses the ability to increase crop productivity and improve field efficiency by precisely metering and placing crop seeds. Growing high yielding crops not only requires using the right seed variety and rate but also achieving optimal performance with available planter technology. Planter performance depends on using the correct planter and technology (display and rate controller system) setup which consists of determining optimal settings for different planting... J.P. Fulton, K.S. Balkcom, B.V. Ortiz, T.P. Mcdonald, G.L. Pate, S.S. Virk, A. Poncet

2. X-Ray Computed Tomography For State Of The Art Plant And Root Analysis

During the last years, the formerly in medical applications established technique of X-ray computed tomography (CT) is used for non-destructive material analysis as well. Adapting this technique for the visualization and analysis of growth processes of plants above and underneath the soil enables new possibilities in the so called smart agriculture. Using State-of-the-art CT systems the computed 3D volume datasets allows the visualization and virtual analysis of hidden structures like roots... S. Reisinger, N. Uhlmann, R. Hanke, S. Gerth

3. Quantification of Seed Performance: Non-Invasive Determination of Internal Traits Using Computed Tomography

The application of the 3D mean-shift filter to 3D Computed Tomography Data enables the segmentation of internal traits. Specifically in maize seeds this approach gives the opportunity to separate the internal structure, for example the volume of the embryo, the cavities and the low and high dense parts of the starch body. To evaluate the mean-shift filter, the results were compared to the usage of a median-smoothing filter. To show the relevance of the mean-shift extended image pipeline an automatic... J. Claussen, N. Wörlein, N. Uhlmann, S. Gerth

4. A Decision-support Tool to Optimize Mid-season Corn Nitrogen Fertilizer Management from Red, Green, Blue SUAS Images

Corn receives more nitrogen (N) fertilizer per unit area than any other row crop and optimized soil fertility management is needed to help maximize farm profitability. In Arkansas, N fertilizer for corn is delivered in two- or three-split applications. Three-split applications may provide a better match to crop needs and contribute to minimizing yield loss from N deficiency. However, the total amounts are selected based on soil texture and yield goal without accounting for early-season losses... A. Poncet, T. Bui, W. France, T. Roberts, L. Purcell, J. Kelley

5. Prescription Map Creation for Optimal Variable-rate Seeding in Arkansas Fields

Soybean seeding rate selection in Arkansas depends on cultivar, planting date, and soil characteristics. Guidelines were developed to maximize profitability from whole field management and little information is available to optimize smaller-scale management. Nevertheless, Arkansas cropland is expected to be a good candidate for variable-rate seeding (VRS) because of heterogeneous soil parent materials, large field sizes, and added spatial variability introduced by the normalization of land-leveling,... W. France, A. Poncet, U. Sigdel, J. Ross

6. Vegetation Coverage Specific Flower Density Estimation in Blackberry Using Unmanned Aerial Vehicle (UAV) Remote Sensing

The effective management of agricultural systems relies on the utilization of accurate data collection techniques to analyze essential crop attributes to enhance productivity and ensure profits. Data collection procedures for specialty horticultural crops are mostly subjective, time consuming and may not be accurate for management decisions in both phenotypic studies and crop production. Reliable and repeatable standard methods are therefore needed to capture and calculate attributes of horticultural... A. Tagoe, C. Koparan, A. Poncet, D.M. Johnson, M. Worthington, D. Wang