Proceedings :: International Society of Precision Agriculture

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Godinho Silva, S

Ferreira , J

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Authors

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

Precision Crop Protection, Pest, and Plant Health

Remote and Proximal Sensing of Soils and Crops

Type

Oral

Poster

Year

2026

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Filter results2 paper(s) found.
1. 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
2. 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 results2 paper(s) found.

1. 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

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2. 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