Proceedings :: International Society of Precision Agriculture

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Antônio, M

Azevedo, I

Alves, F

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Authors

Saiz-Rubio, V

Diago, M

Tardaguila, J

Gutierrez, S

Rovira-Más, F

Alves, F

Fonteca, V

Guimarães, C

Monteiro, M

Azevedo, I

da Rosa, A

Wagner, N.K

Teixeira, C

Antônio, M

Koch, G

Moura, P.A

Silva, F

Santana, C.C

Topics

Robotics, Guidance and Automation

Agricultural Robotics, Automation, and Mechanization

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Year

2018

2026

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Filter results3 paper(s) found.
1. Canopy Temperature Mapping with a Vineyard Robot The wine industry is a strategic sector in many countries worldwide. High revenues in the wine market typically result in higher investments in specialized equipment, so that producers can introduce disruptive technology for increasing grape production and quality. However, many European producers are approaching retirement age, and therefore the agricultural sector needs a way for attracting young farmers who can assure the smooth transition between generations; digital technology offers an opportunity... V. Saiz-rubio, M. Diago, J. Tardaguila, S. Gutierrez, F. Rovira-más, F. Alves Download paper
2. A Architecture for GNSS-Based Autonomous Navigation in Agricultural Robots Global Navigation Satellite Systems (GNSS), especially when used with real-time correction techniques such as Real-Time Kinematic (RTK), are widely employed in precision agriculture due to their ability to provide accurate absolute positioning. This capability enables georeferenced operations such as planting, selective spraying, and autonomous navigation across large agricultural areas, even in environments with few structural references. In contrast, modern robotic navigation frameworks, such... V. Fonteca, C. Guimarães, M. Monteiro, I. Azevedo, A. Da Rosa, N.K. Wagner, C. Teixeira Download paper
3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana Download paper

Filter results3 paper(s) found.

1. Canopy Temperature Mapping with a Vineyard Robot

The wine industry is a strategic sector in many countries worldwide. High revenues in the wine market typically result in higher investments in specialized equipment, so that producers can introduce disruptive technology for increasing grape production and quality. However, many European producers are approaching retirement age, and therefore the agricultural sector needs a way for attracting young farmers who can assure the smooth transition between generations; digital technology offers an opportunity... V. Saiz-rubio, M. Diago, J. Tardaguila, S. Gutierrez, F. Rovira-más, F. Alves

Download paper

2. A Architecture for GNSS-Based Autonomous Navigation in Agricultural Robots

Global Navigation Satellite Systems (GNSS), especially when used with real-time correction techniques such as Real-Time Kinematic (RTK), are widely employed in precision agriculture due to their ability to provide accurate absolute positioning. This capability enables georeferenced operations such as planting, selective spraying, and autonomous navigation across large agricultural areas, even in environments with few structural references. In contrast, modern robotic navigation frameworks, such... V. Fonteca, C. Guimarães, M. Monteiro, I. Azevedo, A. Da Rosa, N.K. Wagner, C. Teixeira

Download paper

3. Prediction of Olive Productivity Using Machine Learning Associated with Aerial Multispectral and Thermal Imagery

Accurate estimation of productivity in olive orchards is fundamental for agricultural planning, resource optimization, and timely decision-making. Conventional methods for assessing productivity are labor-intensive and limited in their ability to represent spatial variability at field scale. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable and non-destructive approach for predicting productivity at field scale. This study evaluated... M. Antônio, G. Koch, P.A. Moura, F. Silva, C.C. Santana

Download paper