Matt Casella from Richtech discusses the Adam bartending robot, and Chris Padwick from John Deere talks about creating vision models for spraying weeds. Both use NVIDIA technology. Topics include AI-powered robots in hospitality and advancements in robotic innovations in agriculture.
Machine vision for weed detection in agriculture improves precision spraying techniques.
Quality training data sets are vital for accurate machine learning models in agriculture.
Optimizing model performance for real-time decision-making in spraying tasks is essential.
Deep dives
Introduction and Focus on the C-and-Spray Project
Chris Padwick, the Director of Machine Vision Learning at John Deere, discusses the C-and-Spray project. This project aims to use cameras and computer vision on sprayers to detect weeds in fields and effectively target the spraying of herbicides, reducing environmental impact.
Data Collection Challenges and Quality Checks
Creating high-quality training data sets is crucial for training machine learning models in agriculture. Labelling data accurately, especially distinguishing between weeds and crops, requires expert agronomists. Ensuring data quality and variety are key in improving model accuracy.
Model Training and Inference Optimization
Developing models for agricultural applications involves training them with high-quality labeled data. Models need to operate in real time, processing large amounts of image data swiftly to make timing decisions for targeted spraying. The complexity lies in optimizing inference speed at high operational speeds.
Adaptation for New Crops and Regions
Chris Padwick discusses adapting machine learning models for new crops or regions. Data collection efforts span various regions and cropping environments. Iterative model development aims to improve accuracy and address specific challenges, with the focus on rapid iterations and data quality assurance.
Future Enhancements with Jetson Orin and Optimization Strategies
The use of Nvidia hardware, like Jetson Orin, is planned for future iterations of the C-and-Spray project. Enhancements, including reducing VFUs and optimizing throughput, are expected to leverage advanced compute capabilities for efficiency and improved model performance.
In this episode, we talk to Matt Casella from Richtech about the Adam bartending robot, and then to Chris Padwick from John Deere about creating vision models for spraying weeds in the field. Both interviews occurred during the NVIDIA GTC24 event in March, and both companies leverage NVIDIA technology in their robotic solutions.
We also cover the breaking news from the week, including the launch of the new Boston Dynamics Electric Atlas
Richtech Robotics: https://www.richtechrobotics.com/
John Deere Autonomous Solutions: https://www.deere.com/en/sprayers/see-spray/
Boston Dynamics Electric Atlas: https://bostondynamics.com/atlas/
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