For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/01/feeding-the-world-through-embedded-vision-a-presentation-from-john-deere/ Travis Davis, Delivery Manager for the Automation Delivery team with the Intelligent Solutions Group at John Deere, presents the “Feeding the World Through Embedded Vision” tutorial at the September 2020 Embedded Vision Summit. Although it’s not widely known outside of the industry, computer vision is beginning to be used at scale in agriculture, where it is delivering meaningful improvements in efficiency and quality, with the potential for tremendous impact on how our food is grown. In this presentation, Davis introduces deployed agricultural computer vision solutions for harvesting and spraying. He explores key technical challenges that John Deere had to overcome to create these solutions, and highlights the ways in which agricultural vision applications often have requirements that are quite different from those of automotive and commercial applications.

1. © 2020 John Deere
Feeding the World Through
Embedded Vision
Travis Davis
September 2020

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Overview
• John Deere Background
• What makes our solution space challenging
(but awesome)
• Hardware Examples
• Solution Examples
• Production Considerations for Embedded
Vision and Machine Learning
• Key Takeaways
• Q&A
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John Deere Background

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About John Deere
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GLOBAL
GROWTH
COMPLEMENTARY
Turf Equipment
Forestry Equipment
Construction Equipment
Agriculture Equipment

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John Deere Supporting Operations
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Power Systems
Financial Services
Worldwide Parts Services Intelligent Solutions Group

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What makes our solution
space challenging?
Hardware Topics

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John Deere Embedded Vision Hardware
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Monocular Camera Grain Quality Camera
Stereo Camera
Vision Processor

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John Deere Testing – Dust Testing
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John Deere Testing – Cold Water “Splash”
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John Deere Testing – Vibration Test Example
(Early Result – obviously fixed for production)
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John Deere Testing – Vibration Test Example
(Early Results – obviously fixed for production)
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John Deere Temp Testing –Test Examples
(Early Results – obviously fixed for production)
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What makes our solution
space challenging?
Solution Examples

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Challenging Solution Examples – Combine Advisor
Which Crop Types Are These?
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Challenging Solution Examples – Combine Advisor
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Challenging Solution Examples – Combine Advisor
Harvesting in Standing Water
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Challenging Solution Examples – Combine Advisor
Harvesting in Standing Water
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Challenging Solution Examples – Combine Advisor
Weed Seeds
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Challenging Solution Examples – Combine Advisor
Dirt
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Challenging Solution Examples – Active Fill Control
Trailer Varieties
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Production Considerations for
Embedded Vision or Machine
Learning Technologies

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Production Journey Image Processing Challenges – Examples
Image Quality Issue – Can you spot it?
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Production Journey Image Processing Challenges – Examples
Can you spot the difference?
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LED Bin “A”
LED Bin “D”
LED Bin “B”
LED Bin “C”

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Production Journey Image Processing Challenges - Examples
Vibration Impact on Performance if not Addressed Appropriately
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Production Journey Image Processing Challenges – Examples
Vibration Impact on Performance if not Addressed Appropriately
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Key Take-Aways
• Commercializing Image Processing & Machine Learning Is Orders Of Magnitude Harder
Than Doing a Demonstration
• Always Consider Your Environment that the Solution Needs to Operate & How
You Communicate When It’s Not Expecting to Operate
• Hard Decisions Will Need to Be Made to Choose Between Application
Performance and Long Term Performance
• Choose an Image Processing or Machine Learning Design For Future Robustness
• Commercialization of Image Processing and Machine Learning Solutions Require High
Levels of Consistency of Various Components
• John Deere is Helping to Feed the World Using Image Processing and Machine Learning
(It’s an awesome place to be!)
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Q&A

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Resource Slide
Key partners on project examples:
Various hardware and component suppliers
2020 Embedded Vision Summit
“Feeding the World Through Embedded
Vision”
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