Automated Detection and Quantification of Stomata

Simple item page
dc.contributor.advisorHõrak, Hanna, juhendaja
dc.contributor.advisorHaamer, Rain Eric, juhendaja
dc.contributor.authorGorbachenko, Ivan
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkond
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituut
dc.date.accessioned2024-06-17T07:57:14Z
dc.date.available2024-06-17T07:57:14Z
dc.date.issued2024
dc.description.abstractThis 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.
dc.identifier.urihttps://hdl.handle.net/10062/99640
dc.language.isoen
dc.publisherTartu Ülikool
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Estoniaen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/ee/
dc.subjectStomatal detection
dc.subjectMachine learning
dc.subjectYOLOv8
dc.subjectPlant phenotypin, image analysis
dc.subjectImage analysis
dc.subject.otherbakalaureusetöödet
dc.titleAutomated Detection and Quantification of Stomata
dc.title.alternativeÕhulõhede automaatne tuvastamine ja loendamine
dc.typeThesis

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
IvanGorbcahenko_ST.pdf
Size:
9.33 MB
Format:
Adobe Portable Document Format
Download

Collections

Science and Technology - Bachelor's theses. Kuni 2024