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Amaro, R.P

Ard, K

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Authors

Amaro, R.P

Amstalden, F

Berro Filho, C

Duft, D.G

Subramoni, H

Murumkar, A

Ard, K

Shearer, S.A

Radhakrishnan, A

Fulton, J.P

Mundada, K

Topics

Remote and Proximal Sensing of Soils and Crops

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

Type

Oral

Poster

Year

2026

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Filter results2 paper(s) found.
1. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 Imagery Weed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this study,... R.P. Amaro, F. Amstalden, C. Berro Filho, D.G. Duft Download paper
2. 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 results2 paper(s) found.

1. Detection of Weed-Related Anomalies in Sugarcane Fields Using Sentinel-2 Imagery

Weed infestation is one of the main causes of yield losses in agricultural systems, particularly in large-scale crops such as sugarcane. Conventional weed management, based on uniform herbicide application, often ignores the spatial variability of infestations, resulting in higher production costs and environmental impacts. In this context, remote sensing and machine learning techniques are recently being used as a solution for automation and precision in crop monitoring. In this study,... R.P. Amaro, F. Amstalden, C. Berro Filho, D.G. Duft

Download paper

2. 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