61. Jules Salzinger - AIT - Building explainable and generalizable AI Systems for Agriculture

Today on the podcast I have to pleasure to talk to Jules Salzinger, Computer Vision Researcher at the Vision & Automation Center of the AIT, the Austrian Institute of Technology.

Jules will share with us, his newest research on applying computer vision systems that analyze drone videos to perform remote plant phenotyping. This makes it possible to analyze plants growth, but as well how certain plant decease spreads within a field.

We will discuss how the diversity im biology and agriculture makes it challenging to build AI systems that generalize between plants, locations and time.

Jules will explain how in their latest research, they focus on performing experiments that provide insights on how to build effective AI systems for agriculture and how to apply them. All of this with the goal to build scalable AI system and to make their application not only possible but efficient and useful.

## TOC

00:00:00 Beginning

00:03:02 Guest Introduction

00:15:04 Supporting Agriculture with AI

00:22:56 Scalable Plant Phenotyping

00:37:33 Paper: TriNet

00:70:10 Major findings

### References

- Jules Salzinger: https://www.linkedin.com/in/jules-salzinger/

- VAC: https://www.ait.ac.at/en/about-the-ait/center/center-for-vision-automation-control

- https://www.ait.ac.at/en/about-the-ait/center/center-for-vision-automation-control

- AI in Agriculture: https://intellias.com/artificial-intelligence-in-agriculture/

- TriNet: Exploring More Affordable and Generalisable Remote Phenotyping with Explainable Deep Models: https://www.mdpi.com/2504-446X/8/8/407