Proceedings
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| Filter results6 paper(s) found. |
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1. A Multimodal Spectral-Robustness-LLM Pipeline for Non-Destructive Identification of Loropetalum chinense CultivarsProprietary cultivars of ornamental shrub Loropetalum chinense, particularly the visually and spectrally similar ‘Cerise Charm’, ‘Purple Daybreak’, and ‘Red Diamond’, derive their market value from the intensity and stability of anthocyanin pigmentation, a trait that degrades subtly under abiotic stress. Reliance on manual (visual) grading makes the industry vulnerable to these latent, pre-manifestation pigment losses, which are often detected only... P. Sundaravadivel, S. Borah, H. Manjunatha, S.P. Kumpatla, L. Tamil, P. Knight, T. Stroud |
2. Unified Detection and Weight Estimation of Small Fruits Using Multi-Task Vision Models in Precision AgricultureThis work presents a single computer vision model that can perform both object detection and image-level regression from the same input image. Many real applications, especially in agriculture, need information about individual objects as well as a global measurement for the entire image. When analyzing an image of small fruits such as different types of berries, grapes, currants, and muscadine grapes, it may be necessary to detect and classify... P. Sundaravadivel, T. Stroud, S. Borah, B.J. Sampson, P. Knight, S.P. Kumpatla, J.F. Ross |
3. 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 |
4. 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 |
5. 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 |
6. Detection of Plants with Xylella fastidiosa in Olive Orchards Using Aerial Multispectral, Thermal Imagery and Machine LearningEarly detection of Xylella fastidiosa in olive orchards remains a significant phytosanitary challenge due to the difficulty of identifying infected plants during the initial symptom development phase. Remote sensing using unmanned aerial vehicles (UAVs) combined with machine learning techniques offers a scalable approach for disease monitoring at field scale. This study evaluated the potential of spectral indices derived from multispectral and thermal imagery for classifying the occurrence of... F. Silva, M. Antônio, G. Koch, P.A. Moura, C.C. Santana |