Automated Detection and Quantification of Stomata
Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
This thesis presents an approach for the automated detection and quantification of stomata using
machine learning techniques. The study focuses on employing the YOLOv8 model to analyse video
data of leaf epidermal imprints, significantly improving the efficiency and accuracy of stomatal
detection compared to traditional manual methods. The results highlight the model's ability to
handle varying focal depths within video frames, ensuring consistent stomatal counts. Future
research directions include expanding the dataset and incorporating advanced image analysis
techniques to further enhance detection accuracy.
Description
Keywords
Stomatal detection, Machine learning, YOLOv8, Plant phenotypin, image analysis, Image analysis