VINE: Precision Agriculture Data Platform & Digital Twin

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, Thomas A. DeFanti

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, Thomas A. DeFanti

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, Thomas A. DeFanti

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, Thomas A. DeFanti

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.