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Title & Speakers	Event
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

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

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