Proceedings :: International Society of Precision Agriculture

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Viaggi, D

Wilson, J.W

Vergaray Ormeño, C.E

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Authors

Craker, B

Nieman, S.T

Wilson, J.W

Rhea, S

Nelson, K

Danford, D

Wilson, J.A

Kemp, B

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

ten Caten, A

Vergaray Ormeño, C.E

Henriques, J.A

Reva, M.M

Silva, M.S

Katuwal, Y

Maesano, G

Viaggi, D

Topics

Decision Support Systems, Cloud Platforms, and Open Data Solutions

Remote and Proximal Sensing of Soils and Crops

Drivers and Barriers to Adoption of Precision and Digital Technologies

Type

Oral

Poster

Year

2026

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Filter results5 paper(s) found.
1. Adapt Standard: Enabling Interoperability in Agricultural Field Operations Data Modern agriculture increasingly relies on sophisticated technologies, including precision farming equipment, sensors, laboratory analyses, and farm management software, to generate critical operational data. Despite these advancements, the industry faces significant interoperability challenges, resulting in fragmented data ecosystems that impede optimized decision-making. While ISO 11783 (ISOBUS) successfully facilitates electronic communication at the machinery level, it does not adequately address... B. Craker, S.T. Nieman, J.W. Wilson, S. Rhea, K. Nelson, D. Danford, J.A. Wilson, B. Kemp
2. 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
3. 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
4. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient Attributes Soil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, although... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva
5. Understanding Adoption and Post-Adoption Impacts of Smart Farming Technologies in Italy Smart farming technologies (SFTs) are increasingly promoted as key enablers of agricultural efficiency, resource optimization, and environmental sustainability. However, despite rapid technological advancement, empirical evidence on realized economic and resource-use impacts under real farming conditions remains limited, creating uncertainty about the magnitude and distribution of impacts. Existing evidence remains largely focused on perceived drivers, barriers, and intentions to adopt, rather...

Filter results5 paper(s) found.

1. Adapt Standard: Enabling Interoperability in Agricultural Field Operations Data

Modern agriculture increasingly relies on sophisticated technologies, including precision farming equipment, sensors, laboratory analyses, and farm management software, to generate critical operational data. Despite these advancements, the industry faces significant interoperability challenges, resulting in fragmented data ecosystems that impede optimized decision-making. While ISO 11783 (ISOBUS) successfully facilitates electronic communication at the machinery level, it does not adequately address... B. Craker, S.T. Nieman, J.W. Wilson, S. Rhea, K. Nelson, D. Danford, J.A. Wilson, B. Kemp

2. 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

3. 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

4. Recalibration of Spectral Models Using Spiking Techniques for Predicting Primary Nutrient Attributes

Soil spectral libraries are an important strategy for rapid prediction of soil fertility atributes in digital agriculture projects. However, their predictive performance may decline when models are applied outside the specific conditions for which they were calibrated. Even in regions with similar pedoclimatic characteristics, management practices can limit model accuracy. In this context, spiking-based recalibration has been proposed as a practical strategy to improve model performance, although... A. Ten Caten, V. Ormeño, J.A. Henriques, M.M. Reva, M.S. Silva

5. Understanding Adoption and Post-Adoption Impacts of Smart Farming Technologies in Italy

Smart farming technologies (SFTs) are increasingly promoted as key enablers of agricultural efficiency, resource optimization, and environmental sustainability. However, despite rapid technological advancement, empirical evidence on realized economic and resource-use impacts under real farming conditions remains limited, creating uncertainty about the magnitude and distribution of impacts. Existing evidence remains largely focused on perceived drivers, barriers, and intentions to adopt, rather...