Proceedings :: International Society of Precision Agriculture

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Valiati, J.F

Valente, D.S

Valdez , G.F

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Authors

Felipe dos Santos, A

Alvez, R.Q

Barboza, T.O

Arnosti, M.C

Valdez , G.F

Silveira, G.L

Baltazar, J.D

Coelho, A.L

de Arruda Viana, L

Brandão, A.D

Valente, D.S

Furtado Jr, M.R

Freitas, R

Mello, V.S

Dallegrave, G.D

Farinati Leite, E

Valiati, J.F

Topics

Precision Crop Protection, Pest, and Plant Health

Agricultural Robotics, Automation, and Mechanization

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Type

Poster

Oral

Year

2026

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Authors

Filter results3 paper(s) found.
1. The Influence of Field Geometry on the Operational Stability of Uav-based Spraying The use of spraying drones has expanded rapidly in precision agriculture; however, operational factors such as field geometry may compromise application stability. This study aimed to evaluate the influence of field shape on operational variability and operational capacity during spraying performed with a DJI Agras T100 drone. The experiment was conducted in two fields with distinct geometries: a regular (rectangular) field and an irregularly shaped field, located at the Technology Development... A. Felipe Dos Santos, R.Q. Alvez, T.O. Barboza, M.C. Arnosti, G.F. Valdez , G.L. Silveira
2. Performance of Autonomous Navigation in an Agricultural Tractor Using Pure Pursuit Control and Dubins Path Planning Autopilot systems in agricultural machinery are primarily designed to ensure accurate tracking of predefined routes. However, their performance is strongly influenced by the internal parameters of the control algorithms employed, which may vary according to operational conditions. In this context, computational simulations constitute a valuable tool for investigating these interactions and identifying optimal operating configurations. This study evaluates an autopilot system based on the Pure... J.D. Baltazar, A.L. Coelho, L. De Arruda Viana, A.D. Brandão, D.S. Valente, M.R. Furtado Jr Download paper
3. SPARC-AI: Synthetic Procedural Agricultural Rendering and Annotation Framework for Crop Phenotyping and AI Applications Between 20% and 40% of global agricultural production is lost annually to pests and diseases, generating economic damages estimated at over US$220 billion each year. This persistent challenge underscores the urgent need for scalable, precise, and cost-effective monitoring solutions. In this scenario, Artificial Intelligence (AI) based pathogen detection systems emerge as transformative tools, enabling high-resolution spatial and temporal monitoring of crop health. However, the performance... R. Freitas, V.S. Mello, G.D. Dallegrave, E. Farinati Leite, J.F. Valiati Download paper

Filter results3 paper(s) found.

1. The Influence of Field Geometry on the Operational Stability of Uav-based Spraying

The use of spraying drones has expanded rapidly in precision agriculture; however, operational factors such as field geometry may compromise application stability. This study aimed to evaluate the influence of field shape on operational variability and operational capacity during spraying performed with a DJI Agras T100 drone. The experiment was conducted in two fields with distinct geometries: a regular (rectangular) field and an irregularly shaped field, located at the Technology Development... A. Felipe Dos Santos, R.Q. Alvez, T.O. Barboza, M.C. Arnosti, G.F. Valdez , G.L. Silveira

2. Performance of Autonomous Navigation in an Agricultural Tractor Using Pure Pursuit Control and Dubins Path Planning

Autopilot systems in agricultural machinery are primarily designed to ensure accurate tracking of predefined routes. However, their performance is strongly influenced by the internal parameters of the control algorithms employed, which may vary according to operational conditions. In this context, computational simulations constitute a valuable tool for investigating these interactions and identifying optimal operating configurations. This study evaluates an autopilot system based on the Pure... J.D. Baltazar, A.L. Coelho, L. De Arruda Viana, A.D. Brandão, D.S. Valente, M.R. Furtado Jr

Download paper

3. SPARC-AI: Synthetic Procedural Agricultural Rendering and Annotation Framework for Crop Phenotyping and AI Applications

Between 20% and 40% of global agricultural production is lost annually to pests and diseases, generating economic damages estimated at over US$220 billion each year. This persistent challenge underscores the urgent need for scalable, precise, and cost-effective monitoring solutions. In this scenario, Artificial Intelligence (AI) based pathogen detection systems emerge as transformative tools, enabling high-resolution spatial and temporal monitoring of crop health. However, the performance... R. Freitas, V.S. Mello, G.D. Dallegrave, E. Farinati Leite, J.F. Valiati

Download paper