High-throughput wheat plant phenotyping using deep learning techniques
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Zaji, Amirhossein
issued: 2023-05-05T21:51:43Z
issued: 2023
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Wheat plant phenotyping is crucial for plant breeding and crop management. Traditional methods, however, are laborious and error prone. Computer vision and Deep Learning (DL) can address these issues by quickly and accurately analyzing wheat plant images, but data collection, annotation, and preprocessing for DL models can be costly and time-consuming. This thesis aims to reduce data preparation time for high-throughput wheat plant phenotyping using DL models. It explores the use of readily available sensors like cellphone RGB cameras for image acquisition and develops more efficient and automated annotation and preprocessing methods. The proposed aug- mentation algorithm reduces the number of training samples needed for accurate DL models, while transfer learning and other techniques are investigated to de- crease the amount of labeled data required. A state-of-the-art framework for localizing and counting wheat spikes using dotted annotation datasets and generation algorithms is proposed and evaluated. This approach is faster than bounding box and polygon annotation methods for wheat spike localization and counting. The computational component employs hybrid UNet architectures, and the proposed models demonstrate improved spike localization and counting compared to previous research studies. This research introduces Automatic Object Level Augmentation (AutoOLA), a novel augmentation algorithm that reduces DL model training sample requirements. AutoOLA augments spikes, leaves, stems, and backgrounds in wheat images separately using their optimized augmentation policies. Results indicate that AutoOLA can improve wheat spike counting by 60%. Stereovision cameras, which are more user-friendly, are employed for plant height measurement. An active learning model is used to annotate images from one camera and transfer them to the other camera’s images to expedite the process. Mask R-CNN is utilized for spike localization, and spike heights are estimated using depth layer…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/84580
note: HIGH-THROUGHPUT WHEAT PLANTPHENOTYPING USING DEEP LEARNINGTECHNIQUESbyAmirhossein ZajiB.Sc., Razi University, Kermanshah, IranM.Sc., Bahonar University, Kerman, IranA THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE COLLEGE OF GRADUATE STUDIES(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)May 2023© Amirhossein Zaji, 2023The following individuals certify that they have read, and recommend to the Col-lege of Graduate Studies for acceptance, the thesis/dissertation entitled:HIGH-THROUGHPUT WHEAT PLANT PHENOTYPING USING DEEP LEARNINGTECHNIQUESsubmitted by Amirhossein Zaji in partial fulfillment of the requirements of thedegree of Doctor of Philosophy.Dr. Zheng Liu, School of EngineeringSupervisorDr. Nicolas Peleato, School of EngineeringSupervisory Committee MemberDr. Pankaj Bhowmik, Aquatic and Crop Resource Development, National ResearchCouncil of CanadaSupervisory Committee MemberDr. Hadi Mohammadi, School of EngineeringUniversity ExaminerDr. Ian Stavness, University of SaskatchewanExternal ExamineriiAbstractWheat plant phenotyping is crucial for plant breeding and crop management. Tra-ditional methods, however, are laborious and error prone. Computer vision andDeep Learning (DL) can address these issues by quickly and accurately analyz-ing wheat plant images, but data collection, annotation, and preprocessing for DLmodels can be costly and time-consuming.This thesis aims to reduce data preparation time for high-throughput wheatplant phenotyping using DL models. It explores the use of readily available sen-sors like cellphone RGB cameras for image acquisition and develops more effi-cient and automated annotation and preprocessing methods. The proposed aug-mentation algorithm reduces the number of training samples needed for accurateDL models, while transfer learning and other techniques are investigated to de-crease the amount of labeled data required.A state-of-the-art framework for localizing and …
has type: Thesis/Dissertation
date issued: 2023-09
is shown at: 10.14288/1.0431930
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
type: Text
identifier URI: http://hdl.handle.net/2429/84580