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Monfort, S
Kumpatla, S
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Authors
Liu, Z
Griffin, T
Kirkpatrick, T
Monfort, S
Khalilian, A
Henderson, W
Mueller, J
Kirkpatrick, T
Monfort, S
Overstreet, C
Sridharan, S
Sornapudi, S
Hu, Q
Kumpatla, S
Bier, J
Topics
Spatial Variability in Crop, Soil and Natural Resources
Spatial Variability in Crop, Soil and Natural Resources
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2010
2022
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Authors

Filter results3 paper(s) found.

1. Development Of A System For Site-specific Nematicide Placement In Cotton

Nematode distribution varies significantly in cotton fields. Population density throughout a field is highly correlated to soil texture. Field-wide application of a uniform nematicide rate results in the chemical being applied to areas without nematodes or where nematode densities are below an economic threshold, or the application of sub-effective levels in areas with high nematode densities. The investigators have developed a “Site- Specific Nematicide Placement”... A. Khalilian, W. Henderson, J. Mueller, T. Kirkpatrick, S. Monfort, C. Overstreet

2. Spatial Econometric Approaches to Develop Site-Specific Nematode Management Strategies in Cotton Production

Root-knot nematode infestations tend to be spatially clustered within agricultural... Z. Liu, T. Griffin, T. Kirkpatrick, S. Monfort

3. 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