Proceedings :: International Society of Precision Agriculture

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Heideker, A

Hass Bomfim Vieira, M

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Authors

Heideker, A

Speranza, E.A

Ferreira, E

Silva, D

Kamienski, C

Bianchi, R

Silva, D

Heideker, A

Bianchi, R

Kamienski, C

Topics

Remote and Proximal Sensing of Soils and Crops

Wireless Sensor Networks, Edge Computing, and Farm Connectivity

Type

Oral

Year

2026

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Filter results2 paper(s) found.
1. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in Agriculture Satellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agricultural... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi Download paper
2. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud Continuum The increasing adoption of Internet of Things (IoT) technologies in smart agriculture has resulted in highly heterogeneous environments composed of diverse sensors, communication protocols, and distributed computing layers. Agricultural systems typically operate across the edge–cloud continuum, encompassing field devices, intermediate processing nodes, and cloud-based platforms. While IoT platforms provide essential services for data ingestion and device management, they often face limitations... D. Silva, A. Heideker, R. Bianchi, C. Kamienski Download paper

Filter results2 paper(s) found.

1. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in Agriculture

Satellite remote sensing has become a key data source for precision agriculture, particularly for monitoring vegetation dynamics and soil water stress over large areas. Multispectral satellite imagery enables the computation of vegetation indices, including NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index), which are commonly employed to quantify vegetation health, vigor, and canopy development. However, the practical use of satellite imagery in data-driven agricultural... A. Heideker, E.A. Speranza, E. Ferreira, D. Silva, C. Kamienski, R. Bianchi

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2. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud Continuum

The increasing adoption of Internet of Things (IoT) technologies in smart agriculture has resulted in highly heterogeneous environments composed of diverse sensors, communication protocols, and distributed computing layers. Agricultural systems typically operate across the edge–cloud continuum, encompassing field devices, intermediate processing nodes, and cloud-based platforms. While IoT platforms provide essential services for data ingestion and device management, they often face limitations... D. Silva, A. Heideker, R. Bianchi, C. Kamienski

Download paper