Proceedings :: International Society of Precision Agriculture

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Amaro, R.P

Ard, K

Auzani Biscaino, M.L

Alvim Santos Romani, L

Amstalden, F

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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

Rodigheri, G

da Silva, J

Alvim Santos Romani, L

Garcia Arnal Barbedo, J

Mullich, A

Maldaner, I

, L

Wendt, L

Turchiello, J

Noal Santarem, M

Auzani Biscaino, M.L

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 results4 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
3. Assessing the Potential of Google Satellite Embeddings for Mapping Sugarcane in Brazilian Production Areas The increasing availability of remote sensing (RS) data with higher spatial resolution, combined with advances in artificial intelligence (AI), has been an essential tool in driving the development of the agricultural sector, such as precision agriculture (PA). Among the most relevant information derived from these approaches, crop mapping plays an essential role in crop monitoring, management strategies and yield forecasting. However, the large amount of data required for training classification... G. Rodigheri, J. Da Silva, L. Alvim Santos Romani, J. Garcia Arnal Barbedo Download paper
4. Digital Terrain Modeling and Topographic Smoothing for Levee Optimization in Irrigated Rice Areas Using AgroCAD® and T3rra Cutta© The systematization of lowland areas intended for irrigated rice production requires precise topographic planning to ensure efficiency in water management and operational performance of agricultural activities. Traditional land leveling methods are often based on empirical approaches, which may result in excessive soil movement and inefficient levee configurations. Advances in Precision Agriculture have enabled the integration of high-precision GNSS positioning with digital terrain modeling tools,... A. Mullich, I. Maldaner, L. , L. Wendt, J. Turchiello, M. Noal Santarem, M.L. Auzani Biscaino Download paper

Filter results4 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

3. Assessing the Potential of Google Satellite Embeddings for Mapping Sugarcane in Brazilian Production Areas

The increasing availability of remote sensing (RS) data with higher spatial resolution, combined with advances in artificial intelligence (AI), has been an essential tool in driving the development of the agricultural sector, such as precision agriculture (PA). Among the most relevant information derived from these approaches, crop mapping plays an essential role in crop monitoring, management strategies and yield forecasting. However, the large amount of data required for training classification... G. Rodigheri, J. Da Silva, L. Alvim Santos Romani, J. Garcia Arnal Barbedo

Download paper

4. Digital Terrain Modeling and Topographic Smoothing for Levee Optimization in Irrigated Rice Areas Using AgroCAD® and T3rra Cutta©

The systematization of lowland areas intended for irrigated rice production requires precise topographic planning to ensure efficiency in water management and operational performance of agricultural activities. Traditional land leveling methods are often based on empirical approaches, which may result in excessive soil movement and inefficient levee configurations. Advances in Precision Agriculture have enabled the integration of high-precision GNSS positioning with digital terrain modeling tools,... A. Mullich, I. Maldaner, L. , L. Wendt, J. Turchiello, M. Noal Santarem, M.L. Auzani Biscaino

Download paper