iot | UCSC OSPO

VINE: Precision Agriculture Data Platform & Digital Twin

Thu, 05 Feb 2026 00:00:00 +0000

VINE (Vineyard Intelligence Network & Environment) is an AI/ML research project focused on precision agriculture using the National Research Platform (NRP). This project leverages the innovative demonstration at Iron Horse Vineyards to study how AI and machine learning can optimize agricultural practices through data-driven insights. Students will work with cutting-edge AI/ML technologies, distributed computing on NRP, and large-scale data analysis, while contributing to real-world applications in sustainable agriculture and climate adaptation.

The project involves AI/ML research using agricultural data from Iron Horse Vineyards, leveraging the computational resources of the National Research Platform for training and deploying machine learning models. Students will work with agricultural datasets including sensor data, multi-spectral drone imagery, and historical records, developing models for predictive analytics, computer vision, and time-series forecasting. The integration of NRP’s distributed infrastructure enables scalable AI research that can process large volumes of sensor data, multi-spectral imagery, and historical agricultural records.

Students will gain hands-on experience with AI/ML model development for agricultural applications, learning how to analyze multi-spectral drone imagery, process time-series sensor data, and build predictive models for irrigation scheduling, pest detection, and harvest timing. They will deploy and train models on NRP’s Kubernetes clusters, utilize GPU resources for deep learning workloads, and work with agricultural datasets for comprehensive research. The project emphasizes using distributed computing on NRP to scale AI/ML experiments and create open, shareable datasets for collaborative research.

The platform builds upon the success demonstrated at Iron Horse Vineyards, where AI-driven analytics have shown potential for 10% water use reduction and improved yield optimization. This project aims to advance AI/ML research in precision agriculture by utilizing NRP’s computational capabilities, creating reproducible research that can benefit the broader agricultural and research communities.

VINE / Data Pipeline & Integration

The proposed work includes building data pipelines to ingest, process, and prepare agricultural data from Iron Horse Vineyards and other sources for AI/ML research. Students will develop pipelines to collect sensor data (soil moisture, temperature, CO2, weather), multi-spectral drone imagery, and historical agricultural records. They will create data validation and quality assurance processes, implement data preprocessing for ML model training, and develop data integration workflows that connect agricultural datasets with NRP computational resources. Students will also work on data sharing mechanisms to make processed datasets available for the research community.

Topics: Data Engineering, Time-Series Data, Data Preprocessing, Data Sharing, ML Data Pipelines
Skills: Python, Pandas, NumPy, Data Validation, REST APIs, Docker, Kubernetes, Data Processing
Difficulty: Medium to Hard
Size: Large (350 hours)
Mentors: Mohammad Firas Sada

VINE / AI/ML Models for Agricultural Analytics on NRP

The proposed work includes developing and training machine learning models for agricultural applications using the National Research Platform (NRP). Students will create models for predictive irrigation scheduling based on soil moisture, weather forecasts, and historical data. They will develop computer vision models for analyzing multi-spectral drone imagery to detect plant health, identify pests, and estimate yield. Students will also work on time-series forecasting models for predicting harvest timing and optimizing resource allocation. The project will involve training models on NRP’s GPU clusters, utilizing distributed training capabilities, and deploying models for real-time inference. Students will leverage agricultural datasets for training and validation, and contribute model outputs and insights for the research community.

Topics: Machine Learning, Computer Vision, Time-Series Analysis, Predictive Analytics, Agricultural AI, Distributed Training
Skills: Python, PyTorch/TensorFlow, scikit-learn, OpenCV, Pandas, NumPy, MLOps, NRP Kubernetes, GPU Computing
Difficulty: Hard
Size: Large (350 hours)
Mentors: Mohammad Firas Sada

VINE / Digital Twin & AI-Driven Visualization

The proposed work includes creating AI-enhanced digital twin systems for agricultural sites using computational resources on NRP. Students will develop 3D visualization systems (potentially using Omniverse or similar platforms) to represent vineyards and farms, integrate AI model predictions into the digital twin for real-time insights, and create interactive dashboards for monitoring and analysis. They will implement spatial data processing using ML models to map sensor locations and readings to geographic coordinates, and develop AI-driven simulation capabilities for testing different agricultural strategies (irrigation patterns, planting layouts, etc.) before implementation. Students will deploy visualization services on NRP infrastructure and integrate with agricultural data sources for real-time updates.

Topics: Digital Twin, AI-Enhanced Visualization, GIS, Spatial Data, ML-Driven Simulation, Real-Time Systems
Skills: Python, 3D Graphics (Omniverse/Unity/Blender), GIS tools, WebGL, React/Three.js, ML Integration, NRP Deployment
Difficulty: Hard
Size: Large (350 hours)
Mentors: Mohammad Firas Sada

VINE / Web Dashboard & NRP Integration Platform

The proposed work includes building a comprehensive web dashboard for visualizing agricultural data, AI model predictions, and research insights. Students will develop a full-stack web application using modern frameworks (React, Flask/FastAPI) deployed on the National Research Platform (NRP). The dashboard will display real-time sensor readings, historical trends from agricultural datasets, AI model predictions, and digital twin visualizations. Students will create API endpoints that integrate with NRP computational resources and agricultural data sources, implement role-based access control for researchers, and enable data export/sharing with the broader research community. The platform will support interactive data exploration tools and provide programmatic access to AI/ML models running on NRP.

Topics: Full-Stack Web Development, Data Visualization, API Development, NRP Deployment, ML Model Serving
Skills: React, Flask/FastAPI, PostgreSQL, D3.js/Plotly, Bootstrap/Tailwind CSS, REST APIs, Kubernetes, NRP APIs
Difficulty: Medium to Hard
Size: Large (350 hours)
Mentors: Mohammad Firas Sada

Project Resources

National Research Platform: https://nrp.ai/
Iron Horse Vineyards Project: https://gitlab.nrp-nautilus.io/ihv
Omniverse Integration: https://gitlab.nrp-nautilus.io/omniverse
CENIC Network: https://cenic.org/
CENIC Precision Agriculture Blog: https://nrp.ai/cenic-precision-agriculture-2025

Background

This project builds upon the successful demonstration at Iron Horse Vineyards, where CENIC, UC San Diego, and partners have created a living laboratory for precision agriculture. The VINE project focuses on AI/ML research using the National Research Platform (NRP) for computational resources. By leveraging NRP’s distributed infrastructure and GPU clusters, students can train and deploy sophisticated ML models for agricultural applications. The project works with agricultural datasets from Iron Horse Vineyards and aims to create open, shareable datasets for the research community. This approach creates a scalable, reproducible framework for AI/ML research in precision agriculture that can benefit researchers, educators, and practitioners nationwide.

Network Simulation Bridge • Enabling Interactive Network Models

Wed, 28 Jan 2026 00:00:00 +0000

The Network Simulation Bridge – NSB – is a network co-simulation framework that bridges together applications and network simulators. It enables students, researchers, and developers to prototype their applications and systems on simulated networks. It consists of a message server and client endpoint interfaces which together form a bridge, routing application message payloads through the network simulator. NSB is designed to be extensible through modular interfaces that serve to allow users to contribute new features and modules that suit evolving and emerging use cases. NSB is developed to be application-, network simulator-, and platform-agnostic so that users and developers are empowered to integrate any application front-end with any network simulator back-end, providing versatility and flexibility when used alongside other tools in larger systems and applications.

NSB was created in-house by the Inter-Networking Research Group and is now being developed into a more full-featured open-source tool and ecosystem in partnership with the UCSC OSPO and as part of the NSF Pathways to Enable Open-Source Ecosystems program. In this transition to a more polished and feature-rich product, the next phase of NSB development will involve the engineering of new quality-of-life features, testing and iteration of the core tool itself, and user-centric refinement via implementation in interdisciplinary system models.

Develop a User-Centric Website for NSB

Topics: Web Development Dynamic Updates UX
Skills: web development experience, good communicator, (HTML/CSS), (Javascript)
Difficulty: Moderate
Size: Large
Mentors: Harikrishna Kuttivelil

Develop a clean and welcoming landing page and website for the project. The organization needs to reflect the needs of both users and potential project contributors. This website will be the first impression for people new to the project and should

Specific tasks:

Work with mentors on understanding the context of the project and the expected needs of the users.
Port relevant documentation and tutorials from the repository page, ensuring updates in the repository are reflected in the website.
Study existing open source product websites and draw insights to include in our own design.
Design the structure of the website according to best OS, visual design, and accessibility design practices.
Include visual content that showcases NSB integration and testimonials (if applicable).

Improve the User Experience of NSB

Topics: Software Engineering User-Centric Development Visualization UI/UX Documentation
Skills: package management, toolchain implementation, process automation, technical writing, (visualization), (bash), (Python), (C++)
Difficulty: Moderate
Size: Medium
Mentors: Harikrishna Kuttivelil

Our goal has always been to keep NSB streamlined and out of the way of the users and developers. In line with that, we want our tool to be easily available and installable, and we want the experience of using it to feel minimal and non-intrusive while providing sufficient observability of NSB’s internals for those who want it.

Specific tasks:

Work with mentors and potential users on identifying aspects of the user experience that can refined for better quality-of-life experiences.
Verify and iterate on existing software packaging methods for NSB to ensure that tool setup is stress-free.
Refine and update existing documentation and tutorials to reflect improvements in the setup, installation, and usage processes.
Work with mentors and other contributors to work backwards from what the user wants to see to design the user interface.
Work with other contributors (see below) to develop a Network-in-a-Box experience with NSB.

Create a Network-in-a-Box Experience with NSB

Topics: Software Engineering, Simulation, System Modeling, System Design, Visualization, UI/UX
Skills: software integration and interfacing, toolchain implementation, process automation, C++, (visualization), (LLM-enabled code generation), (technical writing)
Difficulty: Challenging
Size: Large
Mentors: Harikrishna Kuttivelil

NSB was originally designed for networking graduate students to interface with application-layer programs. But since then, there’s been more of an appetite for a simpler network-in-a-box approach that would allow users to quickly deploy baseline or generated network simulations that are ready for use with NSB.

Specific tasks:

Learn how to use one of the major open-source network simulators (ns3 or OMNeT++).
Work with mentors in designing a simpler, minimal user experience of operating NSB.
Develop tools to automatically create network simulations given input parameters (type of network, number of nodes, description of infrastructure).
Create documentation aimed at new users.
Implement or embed network visualizations to enrich the user experience.

Implement Networked System Models to Evaluate Quality of NSB

Topics: System Modeling Simulation System Design Software Development Product Testing
Skills: software integration, good communication, qualitative research, (proficiency in Python and/or C++), (processing scientific and technical literature)
Difficulty: Challenging
Size: Large
Mentors: Harikrishna Kuttivelil

NSB is a relatively new tool and has not been extensively tested outside of the core contributors, who know a bit too much about the tool. We need to better understand what external user and contributor experience will be like, and the best way to do that is to start developing with NSB to build models of connected systems, i.e., sensor networks, smart homes, smart farms, etc.

Specific tasks:

Research academic literature and relevant works to identify relevant distributed applications to model.
Work with mentors and collaborators to plan implementation of selected system models.
Track and report issues and concerns in quality-of-life experiences, critical errors, or difficulties.
Work with mentors and contributors to address issues and concerns.
Refine and update existing documentation and tutorials to reflect improvements in the setup, installation, and usage processes.
Work with other contributors (see below) in reviewing and cross-referencing model implementations.

Model Autonomous Vehicle Networks to Drive New Feature Development in NSB

Topics: System Modeling Simulation System Design Software Development
Skills: requirement-based software design, message parsing interfaces, server-client communication, (proficiency in Python and/or C++), (processing scientific and technical literature)
Difficulty: Challenging
Size: Large
Mentors: Harikrishna Kuttivelil

NSB today serves its named purpose – message relaying. However, modeling complex systems can sometimes involving synchronizing other simulation features, like mobility when dealing with vehivle networks. Implementing a generic layer of being able to synchronize user-defined features across endpoints would be a powerful, enabling feature in NSB. In the process, we may also uncover opportunities for improving the NSB developer experience.

Specific tasks:

Research academic literature and relevant works to identify and design potential autonomous vehicle network models.
Work with mentors and collaborators to iterate on system designs to ensure it serves the purpose of furthering NSB development.
Help mentors design and develop the new feature synchronization feature in NSB, driven by the autonomous vehicle system model.
Develop and iterate feature synchronization, using mobility as the synchronized feature.
Create documentation and tutorials to serve as resources for future users, contributors, and developers.
Work with other contributors (see above) in reviewing and cross-referencing model implementations.