Chris Padwick is Director of Computer Vision Machine Learning at Blue River Technology, a subsidiary of John Deere. Their core product, See & Spray, is a weeding robot that identifies crops and weeds in order to spray only the weeds with herbicide.

Chris and Lukas dive into the challenges of bringing See & Spray to life, from the hard computer vision problem of classifying weeds from crops, to the engineering feat of building and updating embedded systems that can survive on a farming machine in the field. Chris also explains why user feedback is crucial, and shares some of the surprising product insights he's gained from working with farmers.

The complete show notes (transcript and links) can be found here: http://wandb.me/gd-chris-padwick

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Connect with Chris:

📍 LinkedIn: https://www.linkedin.com/in/chris-padwick-75b5761/

📍 Blue River on Twitter: https://twitter.com/BlueRiverTech

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Timestamps:

0:00 Intro

1:09 How does See & Spray reduce herbicide usage?

9:15 Classifying weeds and crops in real time

17:45 Insights from deployment and user feedback

29:08 Why weed and crop classification is surprisingly hard

37:33 Improving and updating models in the field

40:55 Blue River's ML stack

44:55 Autonomous tractors and upcoming directions

48:05 Why data pipelines are underrated

52:10 The challenges of scaling software & hardware

54:44 Outro

55:55 Bonus: Transporters and the singularity

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Chris Padwick — Smart Machines for More Sustainable Farming 01:00:59 Share

Gradient Dissent: Conversations on AI

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Chris Padwick — Smart Machines for More Sustainable Farming