Proceedings :: International Society of Precision Agriculture

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Langemeier, M

Kaefer Seganfredo, G

Koch, G

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Authors

Colussi, J

Erickson, B

Malone, T

Langemeier, M

Fiechter, C

Kern, L.G

Silveira Pavão, L

Müllich,

Maldaner, I

, L

Sgarbossa, J

Kaefer Seganfredo, G

Rolim Farias da Silva, E

Silveira Farias, M

Antônio, M

Koch, G

Moura, P.A

Silva, F

Santana, C.C

Topics

Drivers and Barriers to Adoption of Precision and Digital Technologies

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Remote and Proximal Sensing of Soils and Crops

Type

Oral

Poster

Year

2026

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Filter results3 paper(s) found.
1. Benchmarking Precision Agriculture Adoption in the United States and Brazil The United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted and... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter Download paper
2. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision Agriculture Precision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias 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. Benchmarking Precision Agriculture Adoption in the United States and Brazil

The United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted and... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter

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2. Spatial Delineation of Site-Specific Management Units Using Vegetation Indices in Precision Agriculture

Precision Agriculture has incorporated Remote Sensing as an essential tool for characterizing the spatial variability of agricultural crops. Among the available spectral indices, vegetation indices stand out for their ability to represent vegetative vigor and spatial patterns associated with crop performance. This study aimed to evaluate the spatial stability of spectral indices obtained from a median composite for management zone delineation and to analyze their agreement with a yield map in... L.G. Kern, L. Silveira Pavão, . Müllich, I. Maldaner, L. , J. Sgarbossa, G. Kaefer Seganfredo, E. Rolim Farias Da Silva, M. Silveira Farias

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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