Proceedings :: International Society of Precision Agriculture

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Myers, D.B

Anup, A

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Kitchen, N.R

Sudduth, K.A

Myers, D.B

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

Information Management and Traceability

Artificial Intelligence (AI) in Agriculture

Type

Poster

Oral

Year

2012

2024

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Filter results2 paper(s) found.
1. Issues in Analysis of Soil-Landscape Effects in a Large Regional Yield Map Collection Yield maps are commonly collected by producers and precision-agriculture service providers and are accumulating in warehouse scale data-stores. A key goal in analysis of yield maps is to understand how climate interacts with soil landscapes to cause spatial and temporal variability in grain yield. However, there are many issues that limit utilization of yield map data for this purpose including: i) yield-landscape inversion between climate years,... N.R. Kitchen, K.A. Sudduth, D.B. Myers Download paper
2. 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 Download paper

Filter results2 paper(s) found.

1. Issues in Analysis of Soil-Landscape Effects in a Large Regional Yield Map Collection

Yield maps are commonly collected by producers and precision-agriculture service providers and are accumulating in warehouse scale data-stores. A key goal in analysis of yield maps is to understand how climate interacts with soil landscapes to cause spatial and temporal variability in grain yield. However, there are many issues that limit utilization of yield map data for this purpose including: i) yield-landscape inversion between climate years,... N.R. Kitchen, K.A. Sudduth, D.B. Myers

Download paper

2. 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

Download paper