Chris Padwick — Smart Machines for More Sustainable Farming

Weights & Biases · Beginner ·🤖 AI Agents & Automation ·4y ago
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 --- Connect with Chris: 📍 LinkedIn: https://www.linkedin.com/in/chris-padwick-75b5761/ 📍 Blue River on Twitter: https://twitter.com/BlueRiverTech --- 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 --- 🎙 Get our podcasts on these platforms: Soundcloud: http://wandb.me/soundcloud Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube
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Chapters (12)

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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