Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Satellite Imagery to Machine Learning Datasets: An Automated System for Soil Water Stress Monitoring in AgricultureSatellite 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 |
2. Automated Leak Classification in Drip Irrigation Systems using Deep Learning and RGB CamerasThe increasing demand for water efficiency in agriculture has driven the development of intelligent irrigation systems. Among them, drip irrigation is widely adopted due to its efficiency; however, these systems are susceptible to leaks caused by mechanical wear, animal interference, and adverse environmental conditions. The manual detection of leaks by human workers in drip irrigation systems is a time-consuming task, difficult to scale, ... F.P. Rivera, C. Kamienski |
3. An AI-Ready Smart Adapter Architecture for Integrating Heterogeneous Agricultural IoT Systems Across the Edge–Cloud ContinuumThe 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 |