Today we’re joined by Dimitris Zermas, a principal scientist at agriscience company Sentera. Dimitris’ work at Sentera is focused on developing tools for precision agriculture using machine learning, including hardware like cameras and sensors, as well as ML models for analyzing the vast amount of data they acquire. We explore some specific use cases for machine learning, including plant counting, the challenges of working with classical computer vision techniques, database management, and data annotation. We also discuss their use of approaches like zero-shot learning and how they’ve taken advantage of a data-centric mindset when building a better, more cost-efficient product.
Data-Centric Zero-Shot Learning for Precision Agriculture with Dimitris Zermas - #615 | The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) - Listen or read transcript on Metacast
