Proceedings :: International Society of Precision Agriculture

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Li, L

Ward, N

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Authors

Shaver, T

Schmer, M

Irmak, S

Van Donk, S

Wienhold, B

Jin, V

Bereuter, A

Francis, D

Rudnick, D

Ward, N

Hendrickson, L

Ferguson, R.B

Adamchuk, V.I

Tasissa, A

Li, L

Murphy, J.M

Topics

Spatial Variability in Crop, Soil and Natural Resources

Artificial Intelligence (AI) in Agriculture

Type

Poster

Oral

Year

2012

2024

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Filter results2 paper(s) found.
1. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn ... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk Download paper
2. Sparse Coding for Classification Via a Locality Regularizer: with Applications to Agriculture High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods become crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination of... A. Tasissa, L. Li, J.M. Murphy Download paper

Filter results2 paper(s) found.

1. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn

... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk

Download paper

2. Sparse Coding for Classification Via a Locality Regularizer: with Applications to Agriculture

High-dimensional data is commonly encountered in various applications, including genomics, as well as image and video processing. Analyzing, computing, and visualizing such data pose significant challenges. Feature extraction methods become crucial in addressing these challenges by obtaining compressed representations that are suitable for analysis and downstream tasks. One effective technique along these lines is sparse coding, which involves representing data as a sparse linear combination of... A. Tasissa, L. Li, J.M. Murphy

Download paper