Proceedings :: International Society of Precision Agriculture

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Gabriel, D

Garnitz, J

Galvão, M.P

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Authors

Gandorfer, M

Vinzent, B

Pfrombeck, J

Garnitz, J

Maidl, F

Silva, L

Gabriel, D

Fiegenbaum, A.S

Griebeler, S.R

Franchi, M

dos Santos, N.S

Rocha, K.F

Vian, A.L

Bendahan, A.B

de Medeiros, R.D

Galvão, M.P

Barreto, G.F

Duarte, D.S

Dantas Oliveira, S.V

Melville, C

Gabriel da Silva Carmo, I.L

Karasinski, M.A

Gil da Silva, B.J

Topics

Profitability and Success Stories in Precision and Digital Agriculture

Education, Training, and Extension for Precision Agriculture

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Oral

Poster

Year

2026

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Authors

Filter results4 paper(s) found.
1. Economic and Ecological Performance and Farm-level Adoption of Market-available Tools for Variable-rate Nitrogen Management in a Region of Small-to-medium-scale Agriculture The proposed contribution combines the results of extensive multi-year field trials on variable rate-nitrogen fertilization (VRN) of winter wheat with the results of a series of farmer surveys, and findings drawn from a government investment subsidy program. All three data sources (field trials, surveys, investment subsidy program) cover roughly the same period and agricultural area. The field trials were conducted from 2023 to 2025, the surveys in 2020, 2022, and 2025, and data on the investment... M. Gandorfer, B. Vinzent, J. Pfrombeck, J. Garnitz, F. Maidl Download paper
2. Are Agronomy Programs Preparing Professionals for Digital Agriculture? A Nationwide Curriculum Analysis in Brazil The digital transformation of agriculture has accelerated the adoption of precision agriculture, artificial intelligence, and data-driven management tools, thereby increasing the demand for professionals equipped with technological and computational competencies. In this context, higher education in Agronomy plays a strategic role in preparing graduates to operate effectively in increasingly digitalized production systems. This study aimed to evaluate the presence of Artificial Intelligence (AI)... L. Silva, D. Gabriel
3. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in Soybean The conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian
4. Weed identification in soybean fields using RGB UAV imagery acquired at different flight altitudes The presence of weeds in agricultural fields is one of the main factors reducing crop productivity due to competition for light, water, and nutrients. In this context, digital agriculture and the use of unmanned aerial vehicles (UAVs) enable the acquisition of high-resolution imagery for detecting and monitoring these weeds. However, increasing flight altitude reduces spatial resolution, compromising the identification of key visual attributes (shape, texture, and edges) and making it more difficult...

Filter results4 paper(s) found.

1. Economic and Ecological Performance and Farm-level Adoption of Market-available Tools for Variable-rate Nitrogen Management in a Region of Small-to-medium-scale Agriculture

The proposed contribution combines the results of extensive multi-year field trials on variable rate-nitrogen fertilization (VRN) of winter wheat with the results of a series of farmer surveys, and findings drawn from a government investment subsidy program. All three data sources (field trials, surveys, investment subsidy program) cover roughly the same period and agricultural area. The field trials were conducted from 2023 to 2025, the surveys in 2020, 2022, and 2025, and data on the investment... M. Gandorfer, B. Vinzent, J. Pfrombeck, J. Garnitz, F. Maidl

Download paper

2. Are Agronomy Programs Preparing Professionals for Digital Agriculture? A Nationwide Curriculum Analysis in Brazil

The digital transformation of agriculture has accelerated the adoption of precision agriculture, artificial intelligence, and data-driven management tools, thereby increasing the demand for professionals equipped with technological and computational competencies. In this context, higher education in Agronomy plays a strategic role in preparing graduates to operate effectively in increasingly digitalized production systems. This study aimed to evaluate the presence of Artificial Intelligence (AI)... L. Silva, D. Gabriel

3. Herbicide Savings and Weed Control Performance Using Green-on-Green Spot Spraying in Soybean

The conventional approach to weed control in large-scale soybean production relies on full-area herbicide spraying, resulting in high chemical input and operational costs. In this context, artificial intelligence-based spot spraying has emerged as a promising alternative to increase efficiency and reduce environmental impact. This study evaluated the performance of a green-on-green spot spraying system based on deep learning algorithms, CORTEX AI (Soybean Model v08), for post-emergence weed control... D. Gabriel, A.S. Fiegenbaum, S.R. Griebeler, M. Franchi, N.S. Dos Santos, K.F. Rocha, A.L. Vian

4. Weed identification in soybean fields using RGB UAV imagery acquired at different flight altitudes

The presence of weeds in agricultural fields is one of the main factors reducing crop productivity due to competition for light, water, and nutrients. In this context, digital agriculture and the use of unmanned aerial vehicles (UAVs) enable the acquisition of high-resolution imagery for detecting and monitoring these weeds. However, increasing flight altitude reduces spatial resolution, compromising the identification of key visual attributes (shape, texture, and edges) and making it more difficult...