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Fernandez, H.J
Fortinis, H
Fischer, H
Feldman, M
Flynn, K
Fischer, H
Ferreira e Silva, J
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Authors
Nze Memiaghe, J.D
Adhikari, K
Smith, D.R
Messiga, A
Flynn, K
Fernandez, H.J
Mazega, M
Fortinis, H
Fischer, H
Luvizotto, C.K
Otoboni, C.E
de Almeida, M.C
Rolon, R
Fischer, H
Otoboni, C.E
Luvizotto, C.K
de Almeida, M.C
Topics
Digital Solutions for Soil Health, Water Quality, and Conservation Practices
Remote and Proximal Sensing of Soils and Crops
Decision Support Systems, Cloud Platforms, and Open Data Solutions
Agricultural Robotics, Automation, and Mechanization
Type
Poster
Oral
Year
2026
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Filter results4 paper(s) found.

1. Using VIS-NIR spectroscopy to predict Water-Extractable Soil Phosphorus content in Texas Vertisols

  Phosphorus (P) is an essential nutrient for plant growth. However, excessive P application can result in P accumulation in agricultural soils, increasing the risk of P losses to water sources. Water-extractable P (Pw) data are essential for assessing the risk of environmental P losses. Investigating field-scale variability of Pw using visible–near infrared spectroscopy (VIS–NIR) remains limited. This study aimed to develop an empirical relationship between Pw, soil chemical...

2. Soil Structural Gradients as Drivers of Multiyear Spectral Variability: A Robust Framework for Management Zone Delineation in Heterogeneous Sugarcane Systems

Persistent soil structural gradients are widely recognized as key drivers of spatial heterogeneity in perennial cropping systems. In the heterogeneous sugarcane production environments of northwestern Argentina (Salta and Jujuy provinces), contrasting sandy and clay-rich sectors generate long-term differences in crop growth potential and resource-use efficiency. Distinguishing stable edaphic influences from transient seasonal variability is essential for reliable precision management. This study... H.J. Fernandez

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3. Mapping Digital Technologies, Cloud Platforms, and Artificial Intelligence in Precision Agriculture: The Software Baseline for a Citrus and Sugarcane Living Lab.

The digital transformation of Precision Agriculture (PA) has been driven by the growing availability of Farm Management Information Systems (FMIS), cloud platforms, and Artificial Intelligence (AI) solutions. This study, linked to the Smart B100 Science for Development Center (CCD-SB100), funded by FAPESP and led by the Agronomic Institute of Campinas (IAC), Faac/Unesp (Bauru), in partnership with FATEC Pompeia, aimed to build a multicriteria matrix (technological inventory) of digital PA solutions... M. Mazega, H. Fortinis, H. Fischer, C.K. Luvizotto, C.E. Otoboni, M.C. De Almeida

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4. A Hardware Classification Matrix for Precision Agriculture: Structuring an On-Farm Living Lab in Brazilian Citrus and Sugarcane Systems

The consolidation of Precision Agriculture (PA) in Brazilian fields depends fundamentally on the physical infrastructure deployed on-farm, including sensors, actuators, embedded controllers, and implements. Although citrus and sugarcane represent pillars of São Paulo's agribusiness, the sector still lacks a systematized inventory that catalogues and classifies PA hardware effectively adopted across different producer profiles. Integrated into the Smart B100 Advanced Research Center... R. Rolon, H. Fischer, C.E. Otoboni, C.K. Luvizotto, M.C. De Almeida