Proceedings :: International Society of Precision Agriculture

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Tavares, T

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Authors

Tavares, T

da Silva, L.A

Otavio da Silva, E

Cherubin, M.R

-, -

Casciello, E

Pozzuto, J

Tavares, T

da Silva, L

de Carvalho , H.W

Topics

Remote and Proximal Sensing of Soils and Crops

Type

Poster

Oral

Year

2026

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Filter results2 paper(s) found.
1. Sensor Fusion and Fitness-for-purpose Screening for SOC Prediction in Vis–NIR Spectral Libraries, Insights from Vis–NIR Plus XRF, Local Calibration, and MRV Thresholds Soil organic carbon (SOC) monitoring requires analytical tools that are accurate and scalable for measurement, reporting and verification process (MRV). In practice, MRV programs also require transparent error interpretation so that sensor-based inference can be linked to decision risk under different field conditions. We assessed whether fusing X-ray fluorescence (XRF) information with vis–NIR spectra improves SOC prediction when models are trained on large spectral libraries and transferred... T. Tavares, L.A. Da Silva, E. , M.R. Cherubin
2. Simplifying Lab Analysis for Mapping Texture and Om Content Via Sensor-based Inference: a Case Study Showing Maps of Eca and Traditional Methods Precision agriculture hinges on soil information at spatial resolutions that capture within-field variability, yet conventional laboratory workflows often constrain sampling density due to cost and turnaround time. In this study, we evaluated a laboratory-based sensor inference service for mapping soil organic matter (OM) and texture (clay and sand) using visible and near-infrared spectroscopy (vis-NIR) and X-ray fluorescence (XRF) spectroscopy. We further evaluated whether resulting maps are... -. -, E. Casciello, J. Pozzuto, T. Tavares, L. Da Silva, H.W. De Carvalho Download paper

Filter results2 paper(s) found.

1. Sensor Fusion and Fitness-for-purpose Screening for SOC Prediction in Vis–NIR Spectral Libraries, Insights from Vis–NIR Plus XRF, Local Calibration, and MRV Thresholds

Soil organic carbon (SOC) monitoring requires analytical tools that are accurate and scalable for measurement, reporting and verification process (MRV). In practice, MRV programs also require transparent error interpretation so that sensor-based inference can be linked to decision risk under different field conditions. We assessed whether fusing X-ray fluorescence (XRF) information with vis–NIR spectra improves SOC prediction when models are trained on large spectral libraries and transferred... T. Tavares, L.A. Da Silva, E. , M.R. Cherubin

2. Simplifying Lab Analysis for Mapping Texture and Om Content Via Sensor-based Inference: a Case Study Showing Maps of Eca and Traditional Methods

Precision agriculture hinges on soil information at spatial resolutions that capture within-field variability, yet conventional laboratory workflows often constrain sampling density due to cost and turnaround time. In this study, we evaluated a laboratory-based sensor inference service for mapping soil organic matter (OM) and texture (clay and sand) using visible and near-infrared spectroscopy (vis-NIR) and X-ray fluorescence (XRF) spectroscopy. We further evaluated whether resulting maps are... -. -, E. Casciello, J. Pozzuto, T. Tavares, L. Da Silva, H.W. De Carvalho

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