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Engle, J
Pittman, J
Bruulsema, T
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Authors
Pittman, J
Kablan, L
Chabot, V
Mailloux, A
Bouchard, M
Fontaine, D
Bruulsema, T
Waltz, L
Khanal, S
Katari, S
Hong, C
Anup, A
Colbert, J
Potlapally, A
Dill, T
Porter, C
Engle, J
Stewart, C
Subramoni, H
Machiraju, R
Ortez, O
Lindsey, L
Nandi, A
Topics
Remote Sensing Applications in Precision Agriculture
Site-Specific Nutrient, Lime and Seed Management
Artificial Intelligence (AI) in Agriculture
Type
Oral
Year
2014
2018
2024
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Filter results3 paper(s) found.

1. Estimation of Vegetative Biomass Using On-the-Go Mobile Sensors

Non-destructive methods for estimation of vegetative biomass have been developed using several remote sensing strategies as well as physical measurement techniques. An effective method for estimating biomass must be at least as accurate as the accepted standard for destructive removal measurement techniques such as a forage harvester or quad harvest strategies. In large part vegetative biomass is considered a function of canopy or plant height. Subsequently, a method or... J. Pittman

2. Variability in Corn Yield Response to Nitrogen Fertilizer in Quebec

Optimizing nitrogen (N) fertilization is important to improve corn yield and to reduce N losses to the environment. The economic optimum nitrogen rate  (EONR) is variable and depends on many factors, including weather conditions and crop management.  The main objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture and spring weather across sites and years in Monteregie, which is the most important with 64% of total area and 69%... L. Kablan, V. Chabot, A. Mailloux, M. Bouchard, D. Fontaine, T. Bruulsema

3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision

Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor.  While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi