DS@GT PlantCLEF 2024: Plant Biodiversity Identification Using Deep Learning

Abstract

This presentation describes our team's (DS@GT) participation in the PlantCLEF 2024 challenge. Our central goal is to develop and submit a working notes paper detailing our approach to the challenge, which tackles the identification of plant biodiversity. Automated plant identification is crucial for addressing the biodiversity crisis and expanding our knowledge. Previous PlantCLEF challenges showcase the power of deep learning in this domain. We aim to improve the accuracy and scalability of automated plant identification. This presentation will outline our proposed solution, discuss lessons learned from leading a machine learning competition team, and offer guidance on crafting a working notes paper for the CLEF conference.

Bio

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Murilo Gustineli is a data Scientist with 4+ years of experience developing machine learning and data engineering infrastructures. He is particularly interested in deep learning, information retrieval, and biodiversity research aiming to improve species identification and conservation efforts. He is in his 5th semester in OMSCS specializing in Machine Learning.