📅 Tuesday\, December 9th \| 🕢 19:30 CET \| 🌐 Online Event 🎤 Speaker: Brian Bønk (Microsoft MVP)

Our DP-600 preparation journey continues with a deep dive into the Microsoft Fabric Data Warehouse — one of the most crucial components for the exam and for building scalable analytics solutions. In this session, Brian Bønk, Fabric expert and MVP, will guide you through the complete lifecycle of working with a Data Warehouse in Fabric, from loading data to securing and monitoring it effectively.

📚 What we’ll cover in Session 2

1️⃣ Get Started with Data Warehouses

Understand how the Fabric Data Warehouse works, its architecture, and when to use it.

2️⃣ Load Data into a Data Warehouse

Learn the core ingestion paths, loading strategies, and performance considerations.

3️⃣ Query a Data Warehouse

Best practices for querying, performance optimization, and using SQL endpoints efficiently.

4️⃣ Monitor a Data Warehouse

Explore the tools and metrics needed to ensure reliability, performance, and cost control.

5️⃣ Secure a Data Warehouse

Learn essential security concepts: roles, permissions, row-level security, and workspace governance.

🎯 Who is this session for?

• Candidates preparing for DP-600
• Data engineers, analytics engineers & BI professionals
• Power BI users transitioning into Fabric engineering
• Anyone wanting a structured, expert-led understanding of Fabric DWH

🔔 Call to Action

👉 RSVP now to reserve your virtual seat! 👉 Share this event with colleagues or community members who are preparing for DP-600 or working with Fabric in production. Let’s grow our communities in Athens and Berlin with high-quality, expert-led learning sessions!

Amazon OpenSearch Service lets you search billions of vectors in milliseconds and with high accuracy to support semantic search and power generative AI. Learn how we're democratizing vector search and accelerating AI application development with vector index GPU-acceleration and auto-optimization on Amazon OpenSearch Service. These new features allow you to build billion-scale vector database in under an hour, and index vectors 10x faster at only a quarter of the cost, while auto-optimizing for search speed, quality and cost savings.

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We admire LLMs for speed and quality, but technology alone doesn’t differentiate. Context does. Generic tools solve generic problems; your cloud infrastructure, cost models, and optimization workflows are specific to your organisation. This talk shows how AI, tuned to your context, delivers value that used to require bespoke development. Unlike static tools or dedicated teams, AI evolves with your environment in real time, both a blessing and a curse for the needs of Financial Services.

An overview of how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Topics include automating and optimizing test design for higher coverage, predicting defects and focusing testing where it matters, enabling intelligent test automation with self-healing scripts, continuously monitoring quality across CI/CD pipelines, and generating realistic, privacy‑compliant test data. Expected outcomes discussed include reducing total cost of quality by up to 25% and accelerating release cycles by 20%.

The Cost of Quality (CoQ) has long been a challenge for QA teams; where rework, production incidents, and defect leakage silently erode profitability. In traditional environments, up to 70% of CoQ comes from failure-related activities. But with AI-driven testing, that balance is shifting. This webinar explores how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Join us to uncover how AI can:

• Automate and optimize test case design for higher coverage and reduced effort.
• Predict defects and focus testing where it matters most.
• Enable intelligent test automation with self-healing scripts.
• Continuously monitor quality across CI/CD pipelines.
• Generate realistic, privacy-compliant test data with minimal manual effort.

We’ll also discuss measurable business outcomes—like reducing total CoQ by up to 25% and accelerating release cycles by 20%.

Learn how to turn testing from a cost center into a strategic value driver by engineering quality from the start.

Agentic AI transforms how data work gets done. By equipping LLMs with governed data and enterprise tools, it can plan, verify, and execute complex workflows — turning months of pipeline work into days while preserving quality and governance.

In this session, former Snowflake and current Genesis engineer, Michael Rainey will share why the founders — former Snowflake executives Matt Glickman and Justin Langseth built Genesis, the first secure, containerized AI agents designed to run natively in the Snowflake AI Data Cloud.

Genesis captures tribal knowledge, proposes mappings, generates dbt-ready pipelines, and prevents downstream breakage — enabling teams to accelerate transformation without adding technical debt.

You’ll learn:

• How Genesis leverages Snowflake’s secure, auditable environment to scale agents safely.
• Architecture choices that preserve institutional knowledge and reduce operational risk.
• Real use cases beyond analysis — from accelerating migrations to delivering production-grade pipelines faster.

The session concludes with a live demo of Genesis inside Snowflake: watch a messy source system converted into a ready-to-run pipeline with lineage, tests, and human-in-the-loop approvals — in minutes, not months.

What We Will Cover

• What is Agentic AI: Core concepts, why agents are not chatbots, and where agents fit in data and workflows.
• What is Snowflake: Services that matter for agents, including governance, compute isolation, Snowpark Container Services, and observability.
• Why Genesis on Snowflake: Data gravity, security boundaries, cost and governance controls, and native execution patterns.
• Getting up and running: Prerequisites, deploying Genesis in Snowpark Container Services, roles and warehouses, connecting sources, enabling lineage and tests, and setting approval steps.
• Use cases and product features: Tribal knowledge capture, schema and mapping proposals, autonomous pipeline generation, impact analysis to prevent breakage, lineage, testing, and approvals.
• Live product demo: End-to-end build from messy source to production pipeline inside Snowflake.

Who Should Attend Data engineers, analytics engineers, platform teams, and architects evaluating governed agents.

Takeaways

• A clear mental model for Agentic AI on Snowflake
• A practical checklist and quick start guide for deploying Genesis natively
• Patterns to reduce technical debt and improve reliability beyond ad hoc analysis

Speakers Michael Rainey

To participate in the event, please complete your free registration here The AI ecosystem is exploding with tools that promise to accelerate delivery, improve quality, and transform the way we work. Yet for many teams, evaluating these tools is overwhelming - flashy demos and marketing claims rarely answer the real questions: Will this work in our context? Can it scale? Is it sustainable? This talk presents a structured framework to cut through the hype and make confident, informed decisions. The framework covers six key dimensions:

1. capabilities,
2. inputs
3. outputs
4. LLM considerations,
5. control
6. cost transparency

The framework gives testers and leaders a holistic lens to evaluate any AI solution. Rather than prescribing which tools to use, it provides a mindset and practical checklist to guide your own assessments. We will look at how these dimensions uncover strengths, risks, and trade-offs: from integration and extensibility, to handling data securely, to balancing automation with human oversight. The framework also highlights how to engage stakeholders, avoid vendor lock-in, and measure long-term value instead of short-term gains. Attendees will leave with clarity, structure, and confidence - equipped to evaluate AI tools objectively and ensure that the ones they adopt truly deliver meaningful impact.

To participate in the event, please complete your free registration here The AI ecosystem is exploding with tools that promise to accelerate delivery, improve quality, and transform the way we work. Yet for many teams, evaluating these tools is overwhelming - flashy demos and marketing claims rarely answer the real questions: Will this work in our context? Can it scale? Is it sustainable? This talk presents a structured framework to cut through the hype and make confident, informed decisions. The framework covers six key dimensions:

1. capabilities,
2. inputs
3. outputs
4. LLM considerations,
5. control
6. cost transparency

The framework gives testers and leaders a holistic lens to evaluate any AI solution. Rather than prescribing which tools to use, it provides a mindset and practical checklist to guide your own assessments. We will look at how these dimensions uncover strengths, risks, and trade-offs: from integration and extensibility, to handling data securely, to balancing automation with human oversight. The framework also highlights how to engage stakeholders, avoid vendor lock-in, and measure long-term value instead of short-term gains. Attendees will leave with clarity, structure, and confidence - equipped to evaluate AI tools objectively and ensure that the ones they adopt truly deliver meaningful impact.

Please, register for the event here

This webinar looks to develop strategies for mastering cost-effective AI agent evaluation at scale using stratified sampling, risk-based testing, and multi-stage screening to cut costs while maintaining enterprise-grade quality control.

This webinar will share key insights on AI agent evaluation and highlight how to:

• Cut evaluation costs through stratified sampling across personas and risk-based prioritisation, focusing resources on high-impact, high-variance scenarios rather than exhaustive testing.
• Build progressive evaluation systems using multi-stage screening that catches obvious issues cheaply, then applies deeper evaluation only where needed - balancing "vibes" for content vs rigorous testing for critical processes.
• Implement enterprise-ready observability with OpenTelemetry telemetry, persona coverage maps, and continuous calibration frameworks that adapt as your agentic platform scales and models evolve

After a 30-minute talk there’ll be a 15-minute Q&A, for which we encourage you to submit questions in advance. A webinar recording and related materials will be shared with all attendees after the event.

Disclaimer: We use Zoom for the webinar stream. The link to the webinar will be sent upon your registration to your email.

Speaker: Glyn Darkin - Global Head of Delivery @ ClearRoute

Having worked in roles ranging from cold caller, marketer and lead engineer in several start-ups to Chief Architect in a Global SI, Glyn Darkin’s jack-of-all-trades aptitude and engineer’s mindset is a winning combination in his role as Global Head of Delivery. Quick to grasp the most convoluted business issue, he has an impressive track record of using technology to solve client problems. His past achievements include developing an award-winning digital mortgage product for a leading bank, and building TescoEntertainment

Fascinated by AI, Glyn’s current obsession is staying ahead of the sweeping changes it brings. At home, he spends his evenings coding to fully understand how the technology will affect not only ClearRoute’s ways of working, but the types of products and platforms we implement for our customers.

Please, register for the event here

This webinar looks to develop strategies for mastering cost-effective AI agent evaluation at scale using stratified sampling, risk-based testing, and multi-stage screening to cut costs while maintaining enterprise-grade quality control.

This webinar will share key insights on AI agent evaluation and highlight how to:

• Cut evaluation costs through stratified sampling across personas and risk-based prioritisation, focusing resources on high-impact, high-variance scenarios rather than exhaustive testing.
• Build progressive evaluation systems using multi-stage screening that catches obvious issues cheaply, then applies deeper evaluation only where needed - balancing "vibes" for content vs rigorous testing for critical processes.
• Implement enterprise-ready observability with OpenTelemetry telemetry, persona coverage maps, and continuous calibration frameworks that adapt as your agentic platform scales and models evolve

After a 30-minute talk there’ll be a 15-minute Q&A, for which we encourage you to submit questions in advance. A webinar recording and related materials will be shared with all attendees after the event.

Disclaimer: We use Zoom for the webinar stream. The link to the webinar will be sent upon your registration to your email.

Speaker: Glyn Darkin - Global Head of Delivery @ ClearRoute

Having worked in roles ranging from cold caller, marketer and lead engineer in several start-ups to Chief Architect in a Global SI, Glyn Darkin’s jack-of-all-trades aptitude and engineer’s mindset is a winning combination in his role as Global Head of Delivery. Quick to grasp the most convoluted business issue, he has an impressive track record of using technology to solve client problems. His past achievements include developing an award-winning digital mortgage product for a leading bank, and building TescoEntertainment

Fascinated by AI, Glyn’s current obsession is staying ahead of the sweeping changes it brings. At home, he spends his evenings coding to fully understand how the technology will affect not only ClearRoute’s ways of working, but the types of products and platforms we implement for our customers.

Summary In this episode Kate Shaw, Senior Product Manager for Data and SLIM at SnapLogic, talks about the hidden and compounding costs of maintaining legacy systems—and practical strategies for modernization. She unpacks how “legacy” is less about age and more about when a system becomes a risk: blocking innovation, consuming excess IT time, and creating opportunity costs. Kate explores technical debt, vendor lock-in, lost context from employee turnover, and the slippery notion of “if it ain’t broke,” especially when data correctness and lineage are unclear. Shee digs into governance, observability, and data quality as foundations for trustworthy analytics and AI, and why exit strategies for system retirement should be planned from day one. The discussion covers composable architectures to avoid monoliths and big-bang migrations, how to bridge valuable systems into AI initiatives without lock-in, and why clear success criteria matter for AI projects. Kate shares lessons from the field on discovery, documentation gaps, parallel run strategies, and using integration as the connective tissue to unlock data for modern, cloud-native and AI-enabled use cases. She closes with guidance on planning migrations, defining measurable outcomes, ensuring lineage and compliance, and building for swap-ability so teams can evolve systems incrementally instead of living with a “bowl of spaghetti.”

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData teams everywhere face the same problem: they're forcing ML models, streaming data, and real-time processing through orchestration tools built for simple ETL. The result? Inflexible infrastructure that can't adapt to different workloads. That's why Cash App and Cisco rely on Prefect. Cash App's fraud detection team got what they needed - flexible compute options, isolated environments for custom packages, and seamless data exchange between workflows. Each model runs on the right infrastructure, whether that's high-memory machines or distributed compute. Orchestration is the foundation that determines whether your data team ships or struggles. ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation in one platform. Whoop and 1Password also trust Prefect for their data operations. If these industry leaders use Prefect for critical workflows, see what it can do for you at dataengineeringpodcast.com/prefect.Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Kate Shaw about the true costs of maintaining legacy systemsInterview IntroductionHow did you get involved in the area of data management?What are your crtieria for when a given system or service transitions to being "legacy"?In order for any service to survive long enough to become "legacy" it must be serving its purpose and providing value. What are the common factors that prompt teams to deprecate or migrate systems?What are the sources of monetary cost related to maintaining legacy systems while they remain operational?Beyond monetary cost, economics also have a concept of "opportunity cost". What are some of the ways that manifests in data teams who are maintaining or migrating from legacy systems?How does that loss of productivity impact the broader organization?How does the process of migration contribute to issues around data accuracy, reliability, etc. as well as contributing to potential compromises of security and compliance?Once a system has been replaced, it needs to be retired. What are some of the costs associated with removing a system from service?What are the most interesting, innovative, or unexpected ways that you have seen teams address the costs of legacy systems and their retirement?What are the most interesting, unexpected, or challenging lessons that you have learned while working on legacy systems migration?When is deprecation/migration the wrong choice?How have evolutionary architecture patterns helped to mitigate the costs of system retirement?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links SnapLogicSLIM == SnapLogic Intelligent ModernizerOpportunity CostSunk Cost FallacyData GovernanceEvolutionary ArchitectureThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day two of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 16 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Field-Ready Vision: Building the Agricultural Image Repository (AgIR) for Sustainable Farming

Data—not models—is the bottleneck in agricultural computer vision. This talk shares how Precision Sustainable Agriculture (PSA) is tackling that gap with the Agricultural Image Repository (AgIR): a cloud bank of high-resolution, labeled images spanning weeds (40+ species), cover crops, and cash crops across regions, seasons, and sensors.

We’ll show how AgIR blends two complementary streams:

(1) semi-field, high-throughput data captured by BenchBot, our open-source, modular gantry that autonomously images plants and feeds a semi-automated annotation pipeline; (2) true field images that capture real environmental variability. Together, they cut labeling cost, accelerate pretraining, and improve robustness in production.

On top of AgIR, we’ve built a data-centric training stack: hierarchical augmentation groups, batch mixers, a stand-alone visualizer for rapid iteration, and a reproducible PyTorch Lightning pipeline. We’ll cover practical lessons from segmentation (crop/weed/residue/water/soil), handling domain shift between semi-field and field scenes, and designing metadata schemas that actually pay off at model time.

About the Speaker

Sina Baghbanijam is a Ph.D. candidate in Electrical and Computer Engineering at North Carolina State University, where his research centers on generative AI, computer vision, and machine learning. His work bridges advanced AI methods with real-world applications across agriculture, medicine, and the social sciences, with a focus on large-scale image segmentation, bias-aware modeling, and data-driven analysis. In addition to his academic research, Sina is currently serving as an Agricultural Image Repository Software Engineering Intern with Precision Sustainable Agriculture, where he develops scalable pipelines and metadata systems to support AI-driven analysis of crop, soil, and field imagery.

Beyond Manual Measurements: How AI is Accelerating Plant Breeding

Traditional plant breeding relies on manual phenotypic measurements that are time-intensive, subjective, and create bottlenecks in variety development. This presentation demonstrates how computer vision and artificial intelligence are revolutionizing plant selection processes by automating trait extraction from simple photographs. Our cloud-based platform transforms images captured with smartphones, drones, or laboratory cameras into instant, quantitative phenotypic data including fruit count, size measurements, and weight estimations.

The system integrates phenotypic data with genotypic, pedigree, and environmental information in a unified database, enabling real-time analytics and decision support through intuitive dashboards. Unlike expensive hardware-dependent solutions, our software-focused approach works with existing camera equipment and standard breeding workflows, making advanced phenotyping accessible to organizations of all sizes.

About the Speaker

Dr. Sharon Inch is a botanist with a PhD in Plant Pathology and over 20 years of experience in horticulture and agricultural research. Throughout her career, she has witnessed firsthand the inefficiencies of traditional breeding methods, inspiring her to found AgriVision Analytics. As CEO, she leads the development of cloud-based computer vision platforms that transform plant breeding workflows through AI-powered phenotyping. Her work focuses on accelerating variety development and improving breeding decision-making through automated trait extraction and data integration. Dr. Sharon Inch is passionate about bridging the gap between advanced technology and practical agricultural applications to address global food security challenges.

AI-assisted sweetpotato yield estimation pipelines using optical sensor data

In this presentation, we will introduce the sensor systems and AI-powered analysis algorithms used in high-throughput sweetpotato post-harvest packing pipelines (developed by the Optical Sensing Lab at NC State University). By collecting image data from sweetpotato fields and packing lines respectively, we aim to quantitatively optimize the grading and yield estimation process, and the planning on storage and inventory-order matching.

We built two customized sensor devices to collect data respectively from the top bins when receiving sweetpotatoes from farmers, and eliminator table before grading and packing process. We also developed a compact instance segmentation pipeline that can run on smart phones for rapid yield estimation in-field with resource limitations. To minimize data privacy concerns and Internet connectivity issues, we try to keep all the analysis pipelines on the edge, which results in a design tradeoff between resource availability and environmental constraints. We will also introduce sensor building with these considerations. The analysis results and real time production information are then integrated into an interactive online dashboard, where stakeholders can leverage to help with inventory-order management and making operational decisions.

About the Speaker

Yifan Wu is a current Ph.D candidate at NC State University working in the Optical Sensing Lab (OSL) supervised by Dr. Michael Kudenov. Research focuses on developing sensor systems and machine learning platforms for business intelligence applications.

An End-to-End AgTech Use Case in FiftyOne

The agricultural sector is increasingly turning to computer vision to tackle challenges in crop monitoring, pest detection, and yield optimization. Yet, developing robust models in this space often requires careful data exploration, curation, and evaluation—steps that are just as critical as model training itself.

In this talk, we will walk through an end-to-end AgTech use case using FiftyOne, an open-source tool for dataset visualization, curation, and model evaluation. Starting with a pest detection dataset, we will explore the samples and annotations to understand dataset quality and potential pitfalls. From there, we will curate the dataset by filtering, tagging, and identifying edge cases that could impact downstream performance. Next, we’ll train a computer vision model to detect different pest species and demonstrate how FiftyOne can be used to rigorously evaluate the results. Along the way, we’ll highlight how dataset-centric workflows can accelerate experimentation, improve model reliability, and surface actionable insights specific to agricultural applications.

By the end of the session, attendees will gain a practical understanding of how to:

- Explore and diagnose real-world agricultural datasets - Curate training data for improved performance - Train and evaluate pest detection models - Use FiftyOne to close the loop between data and models

This talk will be valuable for anyone working at the intersection of agriculture and computer vision, whether you’re building production models or just beginning to explore AgTech use cases.

About the Speaker

Prerna Dhareshwar is a Machine Learning Engineer at Voxel51, where she helps customers leverage FiftyOne to accelerate dataset curation, model development, and evaluation in real-world AI workflows. She brings extensive experience building and deploying computer vision and machine learning systems across industries. Prior to Voxel51, Prerna was a Senior Machine Learning Engineer at Instrumental Inc., where she developed models for defect detection in manufacturing, and a Machine Learning Software Engineer at Pure Storage, focusing on predictive analytics and automation.

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.

Join us for day one of a series of virtual events to hear talks from experts on the latest developments at the intersection of Visual AI in Agriculture.

Date and Time Oct 15 at 9 AM Pacific

Location Virtual. Register for the Zoom.

Paved2Paradise: Scalable LiDAR Simulation for Real-World Perception

Training robust perception models for robotics and autonomy often requires massive, diverse 3D datasets. But collecting and annotating real-world LiDAR point clouds at scale is both expensive and time-consuming, especially when high-quality labels are needed. Paved2Paradise introduces a cost-effective alternative: a scalable LiDAR simulation pipeline that generates realistic, fully annotated datasets with minimal human labeling effort.

The key idea is to “factor the real world” by separately capturing background scans (e.g., fields, roads, construction sites) and object scans (e.g., vehicles, people, machinery). By intelligently combining these two sources, Paved2Paradise can synthesize a combinatorially large set of diverse training scenes. The pipeline involves four steps: (1) collecting extensive background LiDAR scans, (2) recording high-resolution scans of target objects under controlled conditions, (3) inserting objects into backgrounds with physically consistent placement and occlusion, and (4) simulating LiDAR geometry to ensure realism.

Experiments show that models trained on Paved2Paradise-generated data transfer effectively to the real world, achieving strong detection performance with far less manual annotation compared to conventional dataset collection. The approach is not only cost-efficient, but also flexible—allowing practitioners to easily expand to new object classes or domains by swapping in new background or object scans. For ML practitioners working in robotics, autonomous vehicles, or safety-critical perception, Paved2Paradise highlights a practical path toward scaling training data without scaling costs. It bridges the gap between simulation and real-world performance, enabling faster iteration and more reliable deployment of perception models.

About the Speaker

Michael A. Alcorn is a Senior Machine Learning Engineer at John Deere\, where he develops deep learning models for LiDAR and RGB perception in safety-critical\, real-time systems. He earned his Ph.D. in Computer Science from Auburn University\, with a dissertation on improving computer vision and spatiotemporal deep neural networks\, and also holds a Graduate Minor in Mathematics. Michael’s research has been cited by researchers at DeepMind\, Google\, Meta\, Microsoft\, and OpenAI\, among others\, and his (batter\|pitcher)2vec paper was a prize-winner at the 2018 MIT Sloan Sports Analytics Conference. He has also contributed machine learning code to scikit-learn and Apache Solr\, and his GitHub repositories—which have collectively received over 2\,100 stars—have served as starting points for research and production code at many different organizations.

MothBox: inexpensive, open-source, automated insect monitor

Dr. Andy Quitmeyer will talk about the design of an exciting new open source science tool, The Mothbox. The Mothbox is an award winning project for broad scale monitoring of insects for biodiversity. It's a low cost device developed in harsh Panamanian jungles which takes super high resolution photos to then automatically ID the levels of biodiversity in forests and agriculture. After thousands of insect observations and hundreds of deployments in Panama, Peru, Mexico, Ecuador, and the US, we are now developing a new, manufacturable version to share this important tool worldwide. We will discuss the development of this device in the jungles of Panama and its importance to studying biodiversity worldwide.

About the Speaker

Dr. Andy Quitmeyer designs new ways to interact with the natural world. He has worked with large organizations like Cartoon Network, IDEO, and the Smithsonian, taught as a tenure-track professor at the National University of Singapore, and even had his research turned into a (silly) television series called “Hacking the Wild,” distributed by Discovery Networks.

Now, he spends most of his time volunteering with smaller organizations, and recently founded the field-station makerspace, Digital Naturalism Laboratories. In the rainforest of Gamboa, Panama, Dinalab blends biological fieldwork and technological crafting with a community of local and international scientists, artists, engineers, and animal rehabilitators. He currently also advises students as an affiliate professor at the University of Washington.

Foundation Models for Visual AI in Agriculture

Foundation models have enabled a new way to address tasks, by benefitting from emerging capabilities in a zero-shot manner. In this talk I will discuss recent research on enabling visual AI in a zero-shot manner and via fine-tuning. Specifically, I will discuss joint work on RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos.

To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. I will also discuss joint work on enabling multi-modal large language models (MLLMs) to correctly answer prompts that require a holistic spatio-temporal understanding: MLLMs struggle to answer prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip.

However, such a holistic spatio-temporal understanding is important for agents operating in the real world. Our solution involves development of a dedicated data collection pipeline and fine-tuning of an MLLM equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.

About the Speaker

Alex Schwing is an Associate Professor at the University of Illinois at Urbana-Champaign working with talented students on artificial intelligence, generative AI, and computer vision topics. He received his B.S. and diploma in Electrical Engineering and Information Technology from the Technical University of Munich in 2006 and 2008 respectively, and obtained a PhD in Computer Science from ETH Zurich in 2014. Afterwards he joined University of Toronto as a postdoctoral fellow until 2016.

His research interests are in the area of artificial intelligence, generative AI, and computer vision, where he has co-authored numerous papers on topics in scene understanding, inference and learning algorithms, deep learning, image and language processing, and generative modeling. His PhD thesis was awarded an ETH medal and his team’s research was awarded an NSF CAREER award.

Beyond the Lab: Real-World Anomaly Detection for Agricultural Computer Vision

Anomaly detection is transforming manufacturing and surveillance, but what about agriculture? Can AI actually detect plant diseases and pest damage early enough to make a difference? This talk demonstrates how anomaly detection identifies and localizes crop problems using coffee leaf health as our primary example. We'll start with the foundational theory, then examine how these models detect rust and miner damage in leaf imagery.

The session includes a comprehensive hands-on workflow using the open-source FiftyOne computer vision toolkit, covering dataset curation, patch extraction, model training, and result visualization. You'll gain both theoretical understanding of anomaly detection in computer vision and practical experience applying these techniques to agricultural challenges and other domains.

About the Speaker

Paula Ramos has a PhD in Computer Vision and Machine Learning, with more than 20 years of experience in the technological field. She has been developing novel integrated engineering technologies, mainly in Computer Vision, robotics, and Machine Learning applied to agriculture, since the early 2000s in Colombia.