Proceedings
Authors
| Filter results4 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. 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 |
3. Spatial Distribution of Coffee Leaf Miner Infestation and Its Impact on Coffee Fruit Maturation, Yield, and Beverage QualityDifferences in the maturation rate of coffee fruits can be associated with plant stress. The incidence of pests, such as the coffee leaf miner (Leucoptera coffeella), compromises the photosynthetically active area, which can reduce yield and beverage quality. Computer vision can assist in damage reduction by identifying the pest's spatial and temporal behavior. This study aimed to verify, spatially and temporally, the impact of damage caused by the coffee leaf miner on fruit... L.V. Lazzarini, A. , G.P. Cândido, V.M. Nunes, S.M. Hurtado, F.H. Leandro, I.D. Gonçalves |
4. Monitoring the Invasive Grass Eragrostis plana with Artificial Intelligence: A Comparative Study of Hyperspectral Data and Drone-Based Object DetectionThe invasion of exotic plant species is recognized as one of the major threats to biodiversity and ecosystem stability worldwide. In the Brazilian Pampa biome, Eragrostis plana Nees (commonly known as Annoni grass) has become one of the most aggressive invasive species since its introduction in the 1950s. Currently occupying approximately 20% of the native grassland vegetation in the state of Rio Grande do Sul, this species exhibits high adaptive capacity, rapid propagation, and the absence of... S. Camargo, N. Perez, T.S. Lopes, A.R. Silveira |