Proceedings :: International Society of Precision Agriculture

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Subramoni, H

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Authors

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

Subramoni, H

Shearer, S.A

Fulton, J.P

Subramoni, H

Murumkar, A

Ard, K

Shearer, S.A

Radhakrishnan, A

Fulton, J.P

Mundada, K

Topics

Artificial Intelligence (AI) in Agriculture

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Digital Solutions for Soil Health, Water Quality, and Conservation Practices

Type

Oral

Poster

Year

2024

2026

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Filter results3 paper(s) found.
1. 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
2. ICICLE: A Generic Cyberinfrastructure Pipeline for AI-Driven Digital Agriculture Processing Digital agriculture suffers from fragmented data and processing tools, restricting our ability to derive consistent, scalable insights that support precision management. The NSF ICICLE (Intelligent Cyberinfrastructure with Computational Learning in the Environment) project addresses this challenge by developing a generalized cyberinfrastructure framework for AI‑enabled data processing across diverse production systems. Deployed at The Ohio State University, ICICLE provides a unified... H. Subramoni, S.A. Shearer, J.P. Fulton
3. Probfuse Dashboard: Uncertainty-aware Geospatial Fusion For Climate-smart Conservation Recommendations In The Maumee River Basin Nutrient losses from tile-drained row crops in the Maumee River Basin remain a primary driver of harmful algal blooms in western Lake Erie, despite expanding conservation programs and cost-share incentives like the Environmental Quality Incentives Program (EQIP). Existing tools rely on static look-up tables or county averages, lacking probabilistic fusion of multi-source data or uncertainty estimates. This hinders field staff and producers from integrating soils, climate and program rules under... H. Subramoni, A. Murumkar, K. Ard, S.A. Shearer, A. Radhakrishnan, J.P. Fulton, K. Mundada

Filter results3 paper(s) found.

1. 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

2. ICICLE: A Generic Cyberinfrastructure Pipeline for AI-Driven Digital Agriculture Processing

Digital agriculture suffers from fragmented data and processing tools, restricting our ability to derive consistent, scalable insights that support precision management. The NSF ICICLE (Intelligent Cyberinfrastructure with Computational Learning in the Environment) project addresses this challenge by developing a generalized cyberinfrastructure framework for AI‑enabled data processing across diverse production systems. Deployed at The Ohio State University, ICICLE provides a unified... H. Subramoni, S.A. Shearer, J.P. Fulton

3. Probfuse Dashboard: Uncertainty-aware Geospatial Fusion For Climate-smart Conservation Recommendations In The Maumee River Basin

Nutrient losses from tile-drained row crops in the Maumee River Basin remain a primary driver of harmful algal blooms in western Lake Erie, despite expanding conservation programs and cost-share incentives like the Environmental Quality Incentives Program (EQIP). Existing tools rely on static look-up tables or county averages, lacking probabilistic fusion of multi-source data or uncertainty estimates. This hinders field staff and producers from integrating soils, climate and program rules under... H. Subramoni, A. Murumkar, K. Ard, S.A. Shearer, A. Radhakrishnan, J.P. Fulton, K. Mundada