Proceedings :: International Society of Precision Agriculture

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Guimarães Moreira, S

Gavilan, B.Q

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Authors

Costa Barboza, T

Batista da Silva, W

Guimarães Moreira, S

Godinho Silva, S

Lacerda, L

Felipe dos Santos, A

Gavilan, B.Q

Gonzalez Aguilera, C

Topics

Remote and Proximal Sensing of Soils and Crops

Artificial Intelligence, Big Data, and Advanced Analytics in Agriculture

Type

Poster

Year

2026

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Filter results2 paper(s) found.
1. 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
2. Edge AI–Driven Soil Sensing and Fertilization Prediction for Solanum betaceum The growing demand for data-driven fertilization strategies in high-value perennial crops has fostered the development of intelligent systems capable of supporting decision-making directly in the field. In the case of tree tomato (Solanum betaceum), fertilization is commonly performed based on fixed schedules or empirical criteria, which often fail to account for soil dynamics and nutrient variability. This study investigates the feasibility of an embedded artificial intelligence system that integrates...

Filter results2 paper(s) found.

1. 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

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2. Edge AI–Driven Soil Sensing and Fertilization Prediction for Solanum betaceum

The growing demand for data-driven fertilization strategies in high-value perennial crops has fostered the development of intelligent systems capable of supporting decision-making directly in the field. In the case of tree tomato (Solanum betaceum), fertilization is commonly performed based on fixed schedules or empirical criteria, which often fail to account for soil dynamics and nutrient variability. This study investigates the feasibility of an embedded artificial intelligence system that integrates...