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Andrade, P
Parkash, V
Sher, M
Tahir, M
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Authors
Tahir, M
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
Alahe, M
Kemeshi, J.O
Chang, Y
Won, K
Yang, X
Sher, M
Topics
Sensor Application in Managing In-season Crop Variability
Precision Dairy and Livestock Management
Applications of Unmanned Aerial Systems
Edge Computing and Cloud Solutions
Type
Poster
Oral
Year
2012
2018
2022
2024
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Filter results5 paper(s) found.

1. Model for Remote Estimation of Nitrogen Contents of Corn Leaf Using Hyper-Spectral Reflectance under Semi-Arid Condition.

Accuracy and precision of nitrogen estimation can be improved by hyperspectral remote sensing that leads... M. Tahir

2. 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

3. 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

4. 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

5. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing Devices

Smart Agriculture (SA) has captured the interest of both the agricultural business and the scientific community in recent years. Overall, SA aims to help the agricultural and food industry to avoid crop failures, loss of revenues as well as help farmers use inputs (such as fertilizers and pesticides) more efficiently by utilizing Internet of Things (IoT) devices and computing systems. However, rapid digitization and reliance on data-driven technologies create new security threats that can defeat... M. Alahe, J.O. Kemeshi, Y. Chang, K. Won, X. Yang, M. Sher