Proceedings :: International Society of Precision Agriculture

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Alves Henriques, J.P

Abud, H.F

Arantes, C

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Authors

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

Vergaray Ormeño, C.E

ten Caten, A

Alves Henriques, J.P

Maciel Reva, M.A

Sousa Silva, M

Freitas, E

Martins Neto, J

dos Santos e Silva, P

Abud, H.F

Gomes, D.G

Secundino, V.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 results3 paper(s) found.
1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva
3. Evaluation of Transfer Learning in Semantic Segmentation Models for Soybean Seedlings Seed vigor evaluation is fundamental in the quality control of commercial lots, as it is directly associated with the rapid and uniform emergence of seedlings and the initial performance of crops in the field. Traditional methods, although widely used, present limitations such as long execution time, dependence on the evaluator’s experience, and subjectivity. In this context, systems based on Computer Vision emerge as promising alternatives for automating vigor assessment, as they enable... E. Freitas, J. Martins Neto, P. Dos Santos E Silva, H.F. Abud, D.G. Gomes, V.C. Secundino Download paper

Filter results3 paper(s) found.

1. Influence of Spectral Pre-processing and Signal-to-noise Ratio on Soil Fertility Prediction Models

Soil and crop sensing through Vis–NIR spectroscopy is key to expanding the spatial and temporal coverage of precision agriculture initiatives. In this scenario spectral preprocessing and signal-to-noise ratio (SNR) significantly influence the accuracy and stability of soil fertility predictions based on spectroscopy. However, their impact is often underestimated, despite their effect on spectral quality and model performance. This study evaluated the influence of different spectral preprocessing... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

2. Geostatistical Comparison of Soil Fertility Maps Derived from Laboratory Soil Analyses and Spectral Model Predictions

Spatial mapping of soil fertility attributes is a key tool for precision agriculture and efficient management of agricultural fields. Soil spectroscopy is lately being presented as an efficient alternative to soil wet chemistry analysis; however, the spatial reliability of spectrally predicted data must be carefully evaluated. In this study, spatially interpolated maps generated from observed laboratory measurements and spectral predictions, of three soil attributes related to primary soil fertility... V. Ormeño, A. Ten Caten, J.P. Alves Henriques, M.A. Maciel Reva, M. Sousa Silva

3. Evaluation of Transfer Learning in Semantic Segmentation Models for Soybean Seedlings

Seed vigor evaluation is fundamental in the quality control of commercial lots, as it is directly associated with the rapid and uniform emergence of seedlings and the initial performance of crops in the field. Traditional methods, although widely used, present limitations such as long execution time, dependence on the evaluator’s experience, and subjectivity. In this context, systems based on Computer Vision emerge as promising alternatives for automating vigor assessment, as they enable... E. Freitas, J. Martins Neto, P. Dos Santos E Silva, H.F. Abud, D.G. Gomes, V.C. Secundino

Download paper