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Carreira, V

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Authors

Rodrigues , L

Luns, S

Rolim, G

Canata, T

Carreira, V

Maes, W

Rodrigues , L

Tommaselli, A.M

Pereira da Silva, R.P

Carreira, V

Luns, S

Souza, J.B

, B

Conceicao da Silva, L

Pereira da Silva, R.P

Carreira, V

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

UAV-Based Scouting, Imaging, and Targeted Applications

Type

Poster

Year

2026

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Filter results3 paper(s) found.
1. Use of Textural and Spectral Data in Predictive Modeling of Sugarcane Yield Sugarcane is one of the most important crops in Brazil, playing a strategic role in the production of sugar, ethanol, and bioenergy. Efficient monitoring of crop yield is essential for agricultural management and decision-making; however, conventional yield estimation methods are generally labor-intensive, destructive, and inefficient in capturing spatial variability within fields. In this context, the use of remote sensing techniques integrated with machine learning models emerges as a promising... L. Rodrigues , S. Luns, G. Rolim, T. Canata, V. Carreira
2. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical Regions High-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the presence... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira
3. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato Cultivars The growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection was... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira

Filter results3 paper(s) found.

1. Use of Textural and Spectral Data in Predictive Modeling of Sugarcane Yield

Sugarcane is one of the most important crops in Brazil, playing a strategic role in the production of sugar, ethanol, and bioenergy. Efficient monitoring of crop yield is essential for agricultural management and decision-making; however, conventional yield estimation methods are generally labor-intensive, destructive, and inefficient in capturing spatial variability within fields. In this context, the use of remote sensing techniques integrated with machine learning models emerges as a promising... L. Rodrigues , S. Luns, G. Rolim, T. Canata, V. Carreira

2. Sun-view Geometry Causes Hotspot Effect in UAV Imagery During Summer in Tropical Regions

High-resolution imagery acquired by Unmanned Aerial Vehicles (UAVs) is essential for remote sensing applications. However, the high solar elevation around noon during summer in tropical regions produces a hotspot effect when the camera is mapping at nadir. The sun-view alignment generates a bright spot in each image acquired during the flight, which significantly changes the digital number and, consequently, the estimated surface reflectance. The objective of this research was to analyze the presence... W. Maes, L. Rodrigues , A.M. Tommaselli, R.P. Silva, V. Carreira

3. Integration of Spectral Phenological Markers and Artificial Neural Networks for Modeling the Yield of Potato Cultivars

The growing demand for food underscores the importance of essential crops such as potato. In this context, understanding yield dynamics is critical, and digital agriculture emerges as a key tool, enabling more efficient estimation of this variable without the need for destructive sampling. Accordingly, this study aimed to use orbital remote sensing combined with artificial intelligence algorithms to develop more accurate and precise models for potato yield prediction. Field data collection was... S. Luns, J.B. Souza, B. , L. Conceicao Da Silva, R.P. Silva, V. Carreira