Proceedings :: International Society of Precision Agriculture

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De Ross Marchioretto, L

Eissmann Souza, G

Diniz Dalmolin, R.S

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Authors

Moura Bueno, J.

Rech, L.F

Diniz Dalmolin, R.S

de Paula Amaral, L

Buana, I

De Araujo Pedron, F

Nogueira, B

Bender, E

de Carvalho Arruda, D

da Costa Salem, M

Espindola Muller, L

dos Santos Gonçalves Junior, S.R

Eissmann Souza, G

Muller Klassmann, J.V

Gallo, B.B

Bredemeier, C

Gonçalves Junior, S.R

da Costa Salem, M

Eissmann Souza, G

Carvalho de Arruda, D

Bender, E

Nogueira, B

Espindola Muller, L

Gallo, B.B

Bredemeier, C

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Oral

Year

2026

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Filter results3 paper(s) found.
1. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron
2. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction Approach The accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field conditions.... B. Nogueira, E. Bender, D. De Carvalho Arruda, M. Da Costa Salem, L. Espindola Muller, S.R. Dos Santos Gonçalves Junior, G. Eissmann Souza, J.V. Muller Klassmann, B.B. Gallo, C. Bredemeier Download paper
3. Hyperspectral Imagery for Prediction of Leaf Chlorophyll Content in Maize Under the Application of Different Urease Inhibitors Using Machine Learning Urea is the most common and widely used nitrogen (N) source. However, it is highly susceptible to ammonia volatilization losses, especially under favorable climatic conditions. The use of urease inhibitors becomes an important strategy because these compounds slow down the hydrolysis of urea, increasing efficiency in terms of N assimilation, enhancing leaf chlorophyll content, promoting plant growth, and maximizing maize grain yield. In parallel, hyperspectral sensors have emerged as a non-destructive... S.R. Gonçalves Junior, M. Da Costa Salem, G. Eissmann Souza, D. Carvalho De Arruda, E. Bender, B. Nogueira, L. Espindola Muller, B.B. Gallo, C. Bredemeier Download paper

Filter results3 paper(s) found.

1. Field-scale Prediction of Soil Organic Carbon Using Integrated Proximal Sensing and Terrain Covariates

The knowledge of soil organic carbon (SOC) is essential for climate change mitigation strategies, soil security, and management within precision agriculture scenarios in agricultural areas. The use of approaches integrating spectral and magnetic sensor data with topographic covariates has shown promise for predicting SOC along the soil profile. In this context, the study aimed to develop predictive models of SOC content at depth through the integration of proximal sensing data and topographic... J. Moura Bueno, L.F. Rech, R.S. Diniz Dalmolin, L. De Paula Amaral, I. Buana, F. De Araujo Pedron

2. Selection of UAV-based Vegetation Indices for the Prediction of Leaf Chlorophyll Content in Maize Using a Normalized Partial Least Squares Regression (PLSR) Reduction Approach

The accurate monitoring of the nutritional status is essential for optimizing nitrogen (N) fertilization and maximizing maize grain yield. Variations in N availability directly affect agronomic parameters such as leaf chlorophyll content, which can be estimated using optical sensors. This study assessed the effects of urease inhibitors and nitrogen application rates on leaf chlorophyll content and predicted total leaf chlorophyll content in maize using relevant vegetation indices under field conditions.... B. Nogueira, E. Bender, D. De Carvalho Arruda, M. Da Costa Salem, L. Espindola Muller, S.R. Dos Santos Gonçalves Junior, G. Eissmann Souza, J.V. Muller Klassmann, B.B. Gallo, C. Bredemeier

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3. Hyperspectral Imagery for Prediction of Leaf Chlorophyll Content in Maize Under the Application of Different Urease Inhibitors Using Machine Learning

Urea is the most common and widely used nitrogen (N) source. However, it is highly susceptible to ammonia volatilization losses, especially under favorable climatic conditions. The use of urease inhibitors becomes an important strategy because these compounds slow down the hydrolysis of urea, increasing efficiency in terms of N assimilation, enhancing leaf chlorophyll content, promoting plant growth, and maximizing maize grain yield. In parallel, hyperspectral sensors have emerged as a non-destructive... S.R. Gonçalves Junior, M. Da Costa Salem, G. Eissmann Souza, D. Carvalho De Arruda, E. Bender, B. Nogueira, L. Espindola Muller, B.B. Gallo, C. Bredemeier

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