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| Filter results9 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. Precision Irrigation and Beyond: A Multi-Step Zoning Approach for Vineyard Water Management and Wine Quality EnhancementSpatial variability in soil moisture, terrain, and vine physiology presents a major challenge for efficient water management in vineyards. Traditional uniform irrigation often fails to address this heterogeneity, limiting both water use efficiency and wine quality. This study investigated spatial and temporal variations in vine water stress over five consecutive growing seasons, and evaluated zone-specific precision agriculture strategies to support improved irrigation and complementary vineyard... Y. Cohen, I. Bahat, J.M. Grünzweig, V. Alchanatis , O. Keisar, G. Lidor, E. Goldshtein, Y. Netzer |
5. 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 |
6. 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 |
7. Deep Learning Models Applied to Drone Imagery for Counting, Biometry, and Carbon Stock Estimation in Large-scale Macaw Palm (Acrocomia Aculeata) PlantationsMacaw palm is a native Brazilian species with significant productive potential, emerging as a premier candidate for the sustainable replacement of oil palm and as a strategic feedstock for sustainable aviation fuel (SAF) and carbon credit markets. However, as the crop is still in the process of domestication and commercial expansion, there is an urgent need for efficient monitoring technologies that enable the identification of superior individuals and the rigorous auditing of carbon stocks across... P.M. De Sousa, V.A. Galvan, J. Souza, R. . De Oliveira , L.D. Corrêdo, L.D. Pimentel, B.C. Albuquerque |
8. Management Zone Delineation for the Optimization of Nitrogen Use Efficiency in Arabica Coffee CropsPrecision coffee farming requires efficient methods for Nitrogen (N) management—an input of high cost and environmental impact, whose optimization faces challenges due to the topographical characteristics of regions such as the Zona da Mata in Minas Gerais, Brazil. This study evaluates and compares different dimensionality reduction models for agricultural management zone (MZ) delineation, aiming to maximize nitrogen fertilizer use efficiency in Arabica coffee plantations. A dataset comprising... D.N. Nunes, R.P. Oliveira, L.D. Corrêdo, L. Peternelli, A.W. Pedrosa, V.H. Galvan, J. Souza |
9. 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 |