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Data Platforms for Multi-organizational Settings: A Systematic Literature Review with Use Cases and a Reference Architecture

Data has become a key asset in today’s digital economies. To handle vast amounts of diverse data with distinct ownership from different contexts and organizational settings, data platforms—along with the concept of data ecosystems—have recently become a research field of high scholarly interest. This article reviews existing literature on data platforms and introduces a reference architecture designed to enable the deployment of data platforms for secure and trustworthy data storage, analysis, and sharing in multi-organizational settings. The proposed architecture is adaptable to various scenarios and requirements. Through a structured analysis of existing literature, the study identifies five key architectural solutions for multi-organizational data platforms. Through an in-depth examination of selected studies, the findings from the literature analysis on the core technological building blocks are substantiated across the different layers of these platforms. The research also incorporates two use cases from our own research projects where we utilize multi-organizational data platforms. These use cases provide practical insights into architectural building blocks. Based on the findings from the literature review and the two use cases, we derive a comprehensive reference architecture tailored for multi-organizational collaborations.

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Lidar-based Missing plant Detection in Steep Vineyards

In this paper we propose a method for missing and dead plant estimation given a 3D pointcloud of vineyards. Following existing approaches we estimate the plant canopy volume, i.e. the volume of the plant foliage, and perform classification through thresholding. We propose two additional thresholding methods and show their effectiveness by comparing them to an average volume thresholding baseline. Furthermore, we demonstrate our approach on pointclouds acquired with different sensors (lidar, solid-state lidar and camera) and plattforms (UAV and UGV).

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Explaining Seasonal 5G Path Loss in a Vineyard: From Empirical Models to Interpretable Machine Learning

Radio network planning is critical for 5G deployments, particularly for temporary installations in rural areas where terrain and vegetation significantly impact signal propagation. While empirical path loss (PL) models characterize propagation environments through scenario-specific parameters—leading to inherently noisy predictions at individual sites—machine learning (ML) approaches can predict site-specific path loss from multiple features simultaneously. This study conducts a systematic literature review of rural path loss prediction methods and introduces a novel dataset collected via a 5G nomadic measurement platform in a vineyard environment, capturing real-world propagation characteristics. We present a comprehensive comparison of machine learning and interpretable machine learning techniques, demonstrating that vegetation dynamics (quantified through the Normalized Difference Vegetation Index, NDVI) is an important driver of path loss variability when combining data across seasonal campaigns—though not within individual campaigns, where distance dominates. Cross-campaign NDVI transfer, however, is sensitive to satellite resolution, which appears to conflate vine canopy with seasonally managed inter-row ground cover. In cross-campaign transfer, XGBoost proves substantially less susceptible to NDVI-induced degradation than Explainable Boosting Machines (EBM), and a hybrid Log-Normal Shadowing (LNS) and XGBoost model confirms that NDVI captures seasonal variability more effectively than empirical path loss parameters alone. Still, the data captured the expected seasonal trend between April and June 2025, from which our interpretable models derived useful propagation insights. Tree-based models like Random Forest and XGBoost achieved the highest prediction accuracy (𝑅2 up to 0.924 on individual campaigns, 0.891 on combined data, and up to 0.945 (individual) and 0.907 (combined) with antenna pattern-corrected path loss), while explainable boosting machines achieved near-parity (𝑅2 up to 0.919; 0.876 on combined data) with the advantage of interpretability. Among individual campaigns, June—with densest canopy cover—yielded the highest 𝑅2 values. These findings provide actionable insights for optimizing temporary 5G networks in precision agriculture and other rural applications.

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Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of Grapevines

Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy).

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Vineyard3D: Grapevine Localization in 3D Point Clouds

With the growing use of modern sensor technology and precision viticulture in the winemaking process the ability to extract relevant information from large datasets becomes increasingly crucial. The precise monitoring of plant health can require the geolocation of thousands of individual grapevines. We propose a simple method to automate the localization process of grapevine positions in georeferenced LiDAR-recorded 3D point clouds. The method consists of ground removal, grapevine rowd etection in projected 2D depth maps, and plant detection using 2D occurrence maps. This approach is implemented as part of a GUI-based application, Vineyard3D, for practical use. We also provide two registered point cloud datasets of the same vineyard, recorded during winter and summer, as well as manually annotated grapevine positions as ground truth. The proposed method can localize grapevine positions with a absolute average error of up to 7 cm as compared to manually annotated ground truth positions.

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