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dc.contributor.authorSotoodeh, Parsa
dc.date.accessioned2022-11-23T17:29:58Z
dc.date.available2022-11-23T17:29:58Z
dc.date.issued2022-11-04
dc.identifier.citationSotoodeh, Parsa. Field Plant Identification Through Indoor Imagery Using Image Augmentation and Object Detection Algorithms; A Thesis submitted to the Faculty of Graduate Studies of The University of Winnipeg in partial fulfillment of the requirements of the degree of Master of Science, Department of Applied Computer Science. Winnipeg, Manitoba, Canada: University of Winnipeg, 2022. DOI: 10.36939/ir.202211231122.en_US
dc.identifier.urihttps://hdl.handle.net/10680/2020
dc.description.abstractA growing global population and recent changes in climate make it increasingly necessary to incorporate recent advances in machine learning and robotics into agricultural practices. Plant detection is the problem of localizing and classifying all the plants within a given scene. Recent state-of-the-art object detection algorithms show promising results in detecting multiple objects and have great potential for outdoor plant detection. They require, however, a massive annotated plant dataset. It takes a great deal of time and expertise to manually annotate a plant dataset on such a large scale. We propose automating the plant annotation process in this thesis. Synthetic plant image datasets are generated in an outdoor setting by augmenting indoor images of plants that were captured and annotated automatically using a robotic camera system. We examine two different approaches: using image processing techniques to place plants on a soil background and using a generative adversarial model to generate fully synthetic outdoor datasets. We train two different plant detection algorithms on the synthetic datasets and evaluate the results on a manually-annotated outdoor dataset. Our best-performing dataset shows promising results for adoption in larger-scale automatic outdoor plant dataset annotation.en_US
dc.language.isoenen_US
dc.publisherUniversity of Winnipegen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPlant detectionen_US
dc.subjectObject detectionen_US
dc.subjectAgricultureen_US
dc.subjectAdversarial algorithmsen_US
dc.titleField Plant Identification Through Indoor Imagery Using Image Augmentation and Object Detection Algorithmsen_US
dc.typeThesisen_US
dc.description.degreeMaster of Science in Applied Computer Scienceen_US
dc.publisher.grantorUniversity of Winnipegen_US
dc.identifier.doi10.36939/ir.202211231122en_US
thesis.degree.disciplineApplied Computer Science
thesis.degree.levelmasters
thesis.degree.nameMaster of Science in Applied Computer Science
thesis.degree.grantorUniversity of Winnipeg


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