Proceedings :: International Society of Precision Agriculture

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Ferreira , J

Furtado Junior, M.R

Freire de Oliveira, M

Fagundes, F

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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

Gebler, H.F

Grego, C.R

Rodrigues, G.C

Pereira, A

Fagundes, F

Costalonga Vargas, B

Furtado Junior, M.R

Paula, G.O

Coelho, A.L

Moreira, M.C

Carvalho, F.R

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

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Type

Oral

Poster

Year

2026

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Filter results4 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. Producing Ordinary Kriging Interpolated Maps for Biomass Observation Through Values Captured with NDVI and NDRE Imagery. Geostatistics is a well-established method in the scientific community for aiding decision-making in situations with spatial dependence. Generally, the methodology adopted for interpolating fertility maps is the use of data from soil sampling on the property, generating representative thematic maps. However, the number of samples required for this methodology can be problematic when the analysis site is a small farm or one divided into multiple plots, common scenarios in Brazilian coffee farming. Considering... H.F. Gebler, C.R. Grego, G.C. Rodrigues, A. Pereira, F. Fagundes
4. Evaluation of Variable-Rate Application of Growth Regulator in Cotton The application of plant growth regulators in cotton is widely used to control excessive vegetative growth and improve plant architecture. However, the spatial variability present in agricultural fields may reduce the efficiency of conventional fixed-rate application, making variable-rate application (VRA) a promising alternative. The objective of this study was to evaluate the effect of fixed-rate and variable-rate application of the growth regulator mepiquat chloride on plant height, leaf area... B. Costalonga Vargas, M.R. Furtado Junior, G.O. Paula, A.L. Coelho, M.C. Moreira, F.R. Carvalho Download paper

Filter results4 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

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3. Producing Ordinary Kriging Interpolated Maps for Biomass Observation Through Values Captured with NDVI and NDRE Imagery.

Geostatistics is a well-established method in the scientific community for aiding decision-making in situations with spatial dependence. Generally, the methodology adopted for interpolating fertility maps is the use of data from soil sampling on the property, generating representative thematic maps. However, the number of samples required for this methodology can be problematic when the analysis site is a small farm or one divided into multiple plots, common scenarios in Brazilian coffee farming. Considering... H.F. Gebler, C.R. Grego, G.C. Rodrigues, A. Pereira, F. Fagundes

4. Evaluation of Variable-Rate Application of Growth Regulator in Cotton

The application of plant growth regulators in cotton is widely used to control excessive vegetative growth and improve plant architecture. However, the spatial variability present in agricultural fields may reduce the efficiency of conventional fixed-rate application, making variable-rate application (VRA) a promising alternative. The objective of this study was to evaluate the effect of fixed-rate and variable-rate application of the growth regulator mepiquat chloride on plant height, leaf area... B. Costalonga Vargas, M.R. Furtado Junior, G.O. Paula, A.L. Coelho, M.C. Moreira, F.R. Carvalho

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