Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Godinho Silva, S

Ferreira , J

Freire de Oliveira, M

» Add more authors

Add filter to result:

Authors

Alves de Araújo, G

Costa Souza, J.B

Freire de Oliveira, M

Ortiz, B.V

Luns Hatum de Almeida, S

Felipe dos Santos, A

Pereira da Silva, R.P

Françani, A.O

Zhao, L

Ferreira , J

Yan, J

Ferreira, E.J

Jorge, L.A

Costa Barboza, T

Batista da Silva, W

Guimarães Moreira, S

Godinho Silva, S

Lacerda, L

Felipe dos Santos, A

Topics

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Precision Crop Protection, Pest, and Plant Health

Remote and Proximal Sensing of Soils and Crops

Type

Oral

Poster

Year

2026

Home » Authors » Results

Authors

Filter results3 paper(s) found.
1. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...
2. Characterizing Cross-Crop Stink Bug Spectral Signatures from Hyperspectral Data Effective crop protection in agricultural production systems requires the ability to detect pest-induced stress in a timely and reliable manner. In large-scale farming systems, stink bugs attack multiple crop species, making cross-crop pest detection a critical capability for scalable monitoring solutions. Rather than developing crop-specific models that require retraining for each species, identifying crop-independent spectral signatures of stink bug infestation enables transferable detection... A.O. Françani, L. Zhao, J. Ferreira , J. Yan, E.J. Ferreira, L.A. Jorge Download paper
3. Assessment of Machine Learning Models for Leaf Chlorophyll Estimation Using Visible-Range Reflectance Chlorophyll content plays a central role in the photosynthetic process directly influencing plant growth, development and yield. However, plant pigment dynamics arise from complex metabolic interactions that are not adequately captured by conventional statistical approaches or traditional laboratory analyses, which are time-consuming and impractical for large-scale field applications. In this context, remote sensing offers a non-destructive alternative for assessing foliar pigments in agricultural... T. Costa Barboza, W. Batista Da Silva, S. Guimarães Moreira, S. Godinho Silva, L. Lacerda, A. Felipe Dos Santos Download paper

Filter results3 paper(s) found.

1. Integrating Management Zones, Artificial Neural Networks and Remote Sensing for Smart Peanut Harvesting

The integration of technologies contributes significantly to agricultural development, especially regarding the rational and more sustainable use of soil. Thus, the use of remote sensing and artificial intelligence techniques combined with precision agriculture can maximize smart harvesting for peanut crops, which face several challenges such as limited harvesting technology, indeterminate growth, and the development of pods below the soil surface. Therefore, this study aimed to develop a peanut...

2. Characterizing Cross-Crop Stink Bug Spectral Signatures from Hyperspectral Data

Effective crop protection in agricultural production systems requires the ability to detect pest-induced stress in a timely and reliable manner. In large-scale farming systems, stink bugs attack multiple crop species, making cross-crop pest detection a critical capability for scalable monitoring solutions. Rather than developing crop-specific models that require retraining for each species, identifying crop-independent spectral signatures of stink bug infestation enables transferable detection... A.O. Françani, L. Zhao, J. Ferreira , J. Yan, E.J. Ferreira, L.A. Jorge

Download paper

3. Assessment of Machine Learning Models for Leaf Chlorophyll Estimation Using Visible-Range Reflectance

Chlorophyll content plays a central role in the photosynthetic process directly influencing plant growth, development and yield. However, plant pigment dynamics arise from complex metabolic interactions that are not adequately captured by conventional statistical approaches or traditional laboratory analyses, which are time-consuming and impractical for large-scale field applications. In this context, remote sensing offers a non-destructive alternative for assessing foliar pigments in agricultural... T. Costa Barboza, W. Batista Da Silva, S. Guimarães Moreira, S. Godinho Silva, L. Lacerda, A. Felipe Dos Santos

Download paper