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Title & Speakers	Event
Data Talks: Transform Your Challenges into Opportunities 2025-05-22 · 16:00 For this iteration of the ClickHouse Meetup in Belgium, we will be joining the Luzmo Data Talks. To register, please visit: https://www.luzmo.com/resources/luzmo-data-talks-transform-your-challenges-into-opportunities Luzmo Data Talks Luzmo Data Talks is a series of events connecting you with our clients and partners who will share their journeys and how they tackle specific challenges. Each session has a different angle: performance, scalability, leveraging AI, and much more. Enjoy a friendly atmosphere with drinks and snacks to make you feel at home while learning directly from your peers. Date and Time: May 22, 18h - 20h but happy to keep the conversation a bit longer Location: Luzmo HQ - Martelarenlaan 38, 3010 Leuven, Belgium Accessibility: Train station and parking under the office Event Program Agenda: 6:00 PM: Welcome 6:20 PM: Introduction 6:30 PM: Case Study by IO Digital, using Luzmo for the Belgian Federal Government 7:00 PM: Presentation by ClickHouse 7:30 PM: Round Table, sharing first insights from our survey "Why do most dashboards get ignored?" with a curated panel 8:00 PM: Closing & Networking	Data Talks: Transform Your Challenges into Opportunities
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
March 26 - Visual AI for Agriculture 2025-03-26 · 16:00 Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture. Register for the Zoom Better Farming Through Embedded AI Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance. About the Speaker Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS. Brewing Better AI: Unlocking Coffee Data with FiftyOne AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows. 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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry. Image Analysis for Plant Phenotyping Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented. About the Speaker Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging. How Open Source Technology will Transform Precision Agriculture There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production. About the Speaker Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.	March 26 - Visual AI for Agriculture
The Future of Data Engineering: AI, LLMs, and Automation 2025-02-26 · 00:27 Gleb Mezhanskiy – guest @ Datafold , Tobias Macey – host Summary In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Gleb Mezhanskiy about Interview IntroductionHow did you get involved in the area of data management?modern data stack is deadwhere is AI in the data stack?"buy our tool to ship AI"opportunities for LLM in DE workflowContact 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 DatafoldCopilotCursor IDEAI AgentsDataChatAI Engineering Podcast EpisodeMetrics LayerEmacsLangChainLangGraphCrewAIThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA AI/ML Data Engineering Data Management Datafold LLM Modern Data Stack Python SQL	Data Engineering Podcast Listen

Data Talks: Transform Your Challenges into Opportunities 2025-05-22 · 16:00

For this iteration of the ClickHouse Meetup in Belgium, we will be joining the Luzmo Data Talks.

To register, please visit: https://www.luzmo.com/resources/luzmo-data-talks-transform-your-challenges-into-opportunities

Luzmo Data Talks

Luzmo Data Talks is a series of events connecting you with our clients and partners who will share their journeys and how they tackle specific challenges. Each session has a different angle: performance, scalability, leveraging AI, and much more. Enjoy a friendly atmosphere with drinks and snacks to make you feel at home while learning directly from your peers.

Date and Time: May 22, 18h - 20h but happy to keep the conversation a bit longer
Location: Luzmo HQ - Martelarenlaan 38, 3010 Leuven, Belgium
Accessibility: Train station and parking under the office

Event Program

Agenda:

6:00 PM: Welcome
6:20 PM: Introduction
6:30 PM: Case Study by IO Digital, using Luzmo for the Belgian Federal Government
7:00 PM: Presentation by ClickHouse
7:30 PM: Round Table, sharing first insights from our survey "Why do most dashboards get ignored?" with a curated panel
8:00 PM: Closing & Networking

Data Talks: Transform Your Challenges into Opportunities

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

March 26 - Visual AI for Agriculture 2025-03-26 · 16:00

Join us for a series of talks from experts on cutting-edge topics at the intersection of AI, ML, computer vision and agriculture.

Register for the Zoom

Better Farming Through Embedded AI

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

About the Speaker

Chris Padwick joined Blue River Technology in 2016 where he was the first machine learning engineer for the See and Spray product. Chris leads the machine learning team for See and Spray, as well as the new product development function at Blue River. While at Blue River, Chris has received numerous awards related to the See and Spray product development including the President’s award, Innovation award, and the Financial Health award as well as 13 patents. Prior to joining Blue River, Chris worked in the remote sensing industry for 12 years at Maxar (formerly DigitalGlobe), MDA and ITT VIS.

Brewing Better AI: Unlocking Coffee Data with FiftyOne

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.

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. During her PhD and Postdoc research, she deployed multiple low-cost, smart edge & IoT computing technologies, such as farmers, that can be operated without expertise in computer vision systems. The central objective of Paula’s research has been to develop intelligent systems/machines that can understand and recreate the visual world around us to solve real-world needs, such as those in the agricultural industry.

Image Analysis for Plant Phenotyping

Predicting plant/crop phenotypic traits which characterize a variety is important information for plant breeders as well as growers. Computer Vision/AI has an increasingly important role to support this process as it is quick, non-destructive, and can be applied in high throughput data collection. Moreover, CV/AI can also assist in the process of protecting the intellectual property of growers who develop novel varieties of ornamentals, such as flowers. In this talk, results from two use cases – in-vivo tomato phenotyping robot and flower variety cataloguing will be presented.

About the Speaker

Manya V. Afonso is a senior researcher at the Biometris (applied mathematics and statistics) and the Vision plus Robotics groups at the Wageningen University and Research, The Netherlands. He received the B.E. degree in electronics and telecommunication engineering from Goa University, India, in 2003, the M.Tech. degree in communication engineering from IIT Delhi, New Delhi, India in 2005, and the Ph.D. degree from the Instituto Superior Tecnico (IST), Lisbon, Portugal, in 2011. He was a researcher with the Institute of Telecommunications (IT) and then a post-doctoral researcher at the Institute for Systems and Robotics (ISR), Lisbon. Afonso’s research interests are image processing and analysis, inverse problems, optimization, machine learning, computer vision, and deep learning with emphasis on application to plant science, agriculture, and medical imaging.

How Open Source Technology will Transform Precision Agriculture

There’s no doubt that advances in vision technology (algorithms, data, computation and software) are transforming the way fields in agriculture are managed. The unit of management is moving from whole fields and farms to individual plants and even insects. Yet the approach taken—generally closed source, proprietary solutions—mean we aren’t innovating as fast as we could, and we have removed the tools for innovation from the key innovators themselves: farmers. Using his experience with the open-source OpenWeedLocator for DIY weed recognition and the WeedAI platform for image data sharing, Guy will discuss how transforming our approach to an open-source model, will benefit companies, farmers, and the future of food production.

About the Speaker

Guy Coleman is a postdoctoral researcher at the University of Copenhagen in the field of weed recognition technologies and evolutionary biology. He completed a PhD in image-based opportunities for weed recognition tools, and is passionate about how open-source development is a more efficient way forward for innovating in agriculture.

March 26 - Visual AI for Agriculture

The Future of Data Engineering: AI, LLMs, and Automation 2025-02-26 · 00:27

Gleb Mezhanskiy – guest @ Datafold , Tobias Macey – host

Summary In this episode of the Data Engineering Podcast Gleb Mezhanskiy, CEO and co-founder of DataFold, talks about the intersection of AI and data engineering. He discusses the challenges and opportunities of integrating AI into data engineering, particularly using large language models (LLMs) to enhance productivity and reduce manual toil. The conversation covers the potential of AI to transform data engineering tasks, such as text-to-SQL interfaces and creating semantic graphs to improve data accessibility, and explores practical applications of LLMs in automating code reviews, testing, and understanding data lineage.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData 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 Gleb Mezhanskiy about Interview IntroductionHow did you get involved in the area of data management?modern data stack is deadwhere is AI in the data stack?"buy our tool to ship AI"opportunities for LLM in DE workflowContact 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 DatafoldCopilotCursor IDEAI AgentsDataChatAI Engineering Podcast EpisodeMetrics LayerEmacsLangChainLangGraphCrewAIThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

AI/ML Data Engineering Data Management Datafold LLM Modern Data Stack Python SQL

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