Proceedings :: International Society of Precision Agriculture

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Rolim, G

Chaer, G.M

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Authors

Rodrigues , L

Luns, S

Rolim, G

Canata, T

Carreira, V

Fernandes Paiva, D

Chaer, G.M

Topics

Predictive Modeling, Phenotyping, and Digital Tools for Decision Support

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Type

Poster

Year

2026

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Filter results2 paper(s) found.
1. Use of Textural and Spectral Data in Predictive Modeling of Sugarcane Yield Sugarcane is one of the most important crops in Brazil, playing a strategic role in the production of sugar, ethanol, and bioenergy. Efficient monitoring of crop yield is essential for agricultural management and decision-making; however, conventional yield estimation methods are generally labor-intensive, destructive, and inefficient in capturing spatial variability within fields. In this context, the use of remote sensing techniques integrated with machine learning models emerges as a promising... L. Rodrigues , S. Luns, G. Rolim, T. Canata, V. Carreira
2. Transforming Agronomic Tables into Continuous Sufficiency and Fertilizer-rate Functions for Digital Recommendation Systems Soil-test interpretation tables and fertilizer recommendation tables are widely used in agronomic practice, but they typically classify results into discrete categories (e.g., very low, low, medium, and high). While this format is suitable for manual consultation, it introduces artificial “jumps” between classes and limits automation when implementing diagnostic and recommendation rules in computerized systems. In this study, we developed a two-step methodology to convert these tables... D. Fernandes Paiva, G.M. Chaer Download paper

Filter results2 paper(s) found.

1. Use of Textural and Spectral Data in Predictive Modeling of Sugarcane Yield

Sugarcane is one of the most important crops in Brazil, playing a strategic role in the production of sugar, ethanol, and bioenergy. Efficient monitoring of crop yield is essential for agricultural management and decision-making; however, conventional yield estimation methods are generally labor-intensive, destructive, and inefficient in capturing spatial variability within fields. In this context, the use of remote sensing techniques integrated with machine learning models emerges as a promising... L. Rodrigues , S. Luns, G. Rolim, T. Canata, V. Carreira

2. Transforming Agronomic Tables into Continuous Sufficiency and Fertilizer-rate Functions for Digital Recommendation Systems

Soil-test interpretation tables and fertilizer recommendation tables are widely used in agronomic practice, but they typically classify results into discrete categories (e.g., very low, low, medium, and high). While this format is suitable for manual consultation, it introduces artificial “jumps” between classes and limits automation when implementing diagnostic and recommendation rules in computerized systems. In this study, we developed a two-step methodology to convert these tables... D. Fernandes Paiva, G.M. Chaer

Download paper