Proceedings

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Carrow, R
Hu, Q
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Authors
Rice, K
Carson, T
Krum, J
Flitcroft, I
Cline, V
Carrow, R
Sridharan, S
Sornapudi, S
Hu, Q
Kumpatla, S
Bier, J
Topics
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Oral
Year
2010
2022
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1. Spatial Mapping Of Penetrometer Resistance On Turfgrass Soils For Site-specific Cultivation

Site-specific management requires site-specific information.  Soil compaction at field capacity is a major stress on recreational turfgrass sites that requires frequent cultivation. Spatial mapping of penetrometer... K. Rice, T. Carson, J. Krum, I. Flitcroft, V. Cline, R. Carrow

2. A Generative Adversarial Network-based Method for High Fidelity Synthetic Data Augmentation

Digital Agriculture has led to new phenotyping methods that use artificial intelligence and machine learning solutions on image and video data collected from lab, greenhouse, and field environments. The availability of accurately annotated image and video data remains a bottleneck for developing most machine learning and deep learning models. Typically, deep learning models require thousands of unique samples to accurately learn a given task. However, manual annotation of a large dataset will... S. Sridharan, S. Sornapudi, Q. Hu, S. Kumpatla, J. Bier