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Uhrmann, F
Abbas, A
Dalla Betta, M.M
Andrade, P
Parkash, V
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Authors
Brockgreitens, J
Bui, M
Abbas, A
Mulla, D
Scholz, O
Uhrmann, F
Gerth, S
Pieger, K
Claußen, J
Dallago, G.M
Figueiredo, D
Santos, R
Andrade, P
Santos, D
Dallago, G.M
Figueiredo, D
Santos, R
Andrade, P
Santschi, D.E
Lacroix, R
Lefebvre, D.M
Pokhrel, A
Virk, S
Snider, J.L
Vellidis, G
Parkash, V
Scholz, O
Uhrmann, F
Weule, M
Meyer, T
Gilson, A
Makarov, J
Hansen, J
Henties, T
Dalla Betta, M.M
Puntel, L
Thompson, L
Mieno, T
Luck, J.D
Cafaro La Menza, N
Paccioretti, P
Topics
Engineering Technologies and Advances
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Precision Dairy and Livestock Management
Applications of Unmanned Aerial Systems
Robotics and Automation with Row and Horticultural Crops
On Farm Experimentation with Site-Specific Technologies
Type
Poster
Oral
Year
2016
2018
2022
2024
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Authors

Filter results7 paper(s) found.

1. Field Sampling and Electrochemical Detection of Nitrate in Agricultural Soils

Nitrate is an essential plant nutrient and is added to farm fields to increase crop yields. While the addition of nitrate is important for production, over-fertilization with nitrate can lead to leaching and contamination of water bodies. Increased nitrate loading in water sources then leads to eutrophication and hypoxia in downstream regions. Many efforts are being made to accurately control nitrate fertilizer additions to fields. Here, we present a soil sampling device that directly samples... J. Brockgreitens, M. Bui, A. Abbas, D. Mulla

2. A Comparison of Three-Dimensional Data Acquisition Methods for Phenotyping Applications

Currently Phenotyping is primarily performed using two-dimensional imaging techniques. While this yields interesting data about a plant, a lot of information is lost using regular cameras. Since a plant is three-dimensional, the use of dedicated 3D-imaging sensors provides a much more complete insight into the phenotype of the plant. Different methods for 3D-data acquisition are available, each with their inherent advantages and disadvantages. These have to be addressed depending on the particular... O. Scholz, F. Uhrmann, S. Gerth, K. Pieger, J. Claußen

3. Exploring Relationships Between Dairy Herd Improvement Metrics in Minas Gerais – Brazil Dairy Herds

The objective of the present study was to apply principal component analysis (PCA) on Brazilian Dairy Herd Improvement (DHI) data to discover the subset of most meaningful variables to describe complete lactations. The Holstein Livestock Breeders Association of Minas Gerais provided data collected between 2005 and 2016 from 122 dairy farms located in the State of Minas Gerais – Brazil. Twelve numerical variables were selected from the original dataset and four additional variables were created.... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D. Santos

4. Relationships Between First Test Day Metrics of First Lactation Cows to Evaluate Transition Period

The objective of this study was to apply principal component analysis (PCA) and multiple correspondence analysis (MCA) on Dairy Herd Improvement (DHI) data of animals on their first lactation to discover the most meaningful set of variables that describe the outcome on the first test day. Data collected over 4 years were obtained from 13 dairy herds located in Québec – Canada. The data set was filtered to contain only information from first test day of animals on their first lactation,... G.M. Dallago, D. Figueiredo, R. Santos, P. Andrade, D.E. Santschi, R. Lacroix, D.M. Lefebvre

5. Potential of UAS Multispectral Imagery for Predicting Yield Determining Physiological Parameters of Cotton

The use of unmanned aerial systems (UAS) in precision agriculture has increased rapidly due to the availability of reliable, low-cost, and high-resolution sensors as well as advanced image processing software. Lint yield in cotton is the product of three physiological parameters: photosynthetically active radiation intercepted by canopy (IPAR), the efficiency of converting intercepted active radiation to biomass (RUE), and the ratio of economic yield to total dry matter (HI). The relationships... A. Pokhrel, S. Virk, J.L. Snider, G. Vellidis, V. Parkash

6. Creating a Comprehensive Software Framework for Sensor-driven Precision Agriculture

Robots and GPS-guided tractors are the backbone of smart farming and precision agriculture. Many companies and vendors contribute to the market, each offering their own customized solutions for common tasks. These developments are often based on vendor-specific, proprietary components, protocols and software. Many small companies that produce sensors, actuators or software for niche applications could contribute their expertise to the global efforts of creating smart farming solutions, if their... O. Scholz, F. Uhrmann, M. Weule, T. Meyer, A. Gilson, J. Makarov, J. Hansen, T. Henties

7. Determining Site-Specific Soybean Optimal Seeding Rate Using On-Farm Precision Experimentation

Ten on-farm precision experiments were conducted in Nebraska during 2018 – 2022 to address the following: i) determine the Economic Optimal Seeding Rates (EOSR), ii) identify the most important site-specific variables influencing the optimal seeding rates for soybeans. Seeding rates ranged from 200,000 to 440,000 seeds ha-1, and treatments were randomized and replicated in blocks across the entire field. The study was implemented using a variable rate prescription. Yield... M.M. Dalla betta, L. Puntel, L. Thompson, T. Mieno, J.D. Luck, N. Cafaro la menza, P. Paccioretti