Data-Centric Zero-Shot Learning for Precision Agriculture with Dimitris Zermas - #615
Update: 2023-02-06
Description
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.
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As someone interested in both data science and agriculture, I found this podcast fascinating. The potential applications for AI in agriculture are vast and exciting, but as the podcast notes, high-quality data annotation is crucial to the success of these technologies. That's why I highly recommend checking out this article on https://www.waybinary.com/types-of-data-annotation-for-ai-applications/, which delves deeper into the importance of data annotation and the different techniques used in the field.