Brian D. Luck
University of Wisconsin-Madison
34 Papers
111 Citations
Brian D. Luck is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Agriculture & Engineering. The author has an hindex of 7, co-authored 31 publications. Previous affiliations of Brian D. Luck include Mississippi State University & Western Kentucky University.
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Papers
Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning
TL;DR: An in-season alfalfa yield prediction using UAV-based hyperspectral images is performed using an ensemble machine learning model developed by combining three widely used base learners including random forest, support vector regression and K-nearest neighbors to demonstrate the efficacy of the proposed ensemble model.
197
Silage review: Recent advances and future technologies for whole-plant and fractionated corn silage harvesting
TL;DR: Results from this review indicate that optimization of harvest maturity, kernel processing, theoretical length of cut, and cutting height improve or maintain the nutritive value and milk production of lactating dairy cows.
169
Assessment of digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin
TL;DR: The survey examined areas of digital application adoption including finance and marketing tools and apps, precision planting and harvesting, sensor applications (soil, livestock, structures/environment), and robotic milking equipment and found adoption to increase with acreage, income, youth, and for females.
91
Reducing pesticide over-application with map-based automatic boom section control on agricultural sprayers
TL;DR: In this article, the authors compare areas treated by a sprayer before and after the addition of an automatic boom section control system and determine if there was a relationship between field shape factors, specifically perimeter-to-area (P/A) ratio, and pesticide over-application.
55
Computational model of methane and ammonia emissions from dairy barns: Development and validation
TL;DR: A steady-state computational fluid dynamics model capable of simulating biologically based generation of methane, ammonia, and heat and their transport within the domain was developed and validated and could be used to predict gaseous emissions under a range of environmental, design, and experimental treatment parameters.
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