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dc.contributor.authorBafandkar, Maryam
dc.date.accessioned2022-05-03T15:53:21Z
dc.date.available2022-05-03T15:53:21Z
dc.date.issued2022-04
dc.identifier.citationBafandkar, Maryam. Exploring Deep Neural Networks for Plant Image Classification; 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, University of Winnipeg. Winnipeg, Manitoba, Canada: University of Winnipeg, 2022. DOI: 10.36939/ir.202205031043.en_US
dc.identifier.urihttps://hdl.handle.net/10680/1993
dc.description.abstractAutomatically distinguishing different types of plant images is a challenging problem relevant to both Botany and Computer Science disciplines. Plant identification at the species level is a computer vision task called fine-grained categorization, which focuses on differentiating between hard-to-distinguish object classes. This classification problem is complicated and challenging because of the lack of annotated data, inter-species similarity, the large-scale features in appearance, and a large number of plant species. A plant classification system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in public computer science education but also in numerous agricultural activities such as automatic detection of cash crops and non-crop plants (called weeds). Furthermore, successful automation of crop and weed identification will lead to the reduction of chemical compounds currently used to eliminate weeds [15]. Deep Convolutional Neural Networks (CNN) can be a solution to perform this computer vision task. In this thesis, seven different CNN models are deployed to classify 1 million images - from the TerraByte dataset - of eleven very similar plant species [13]. This robust approach divides the problem into two main steps: the first step, called the generalist, identifies similar plants and separates them into different groups that contain indistinguishable plant species. The second step, called specialist, is used to classify plants within the groups of indistinguishable plants, including five weed and seven crop species, with high accuracy. The generalist-specialist CNN network shows that the hierarchical network outperforms simple CNN models in terms of accuracy and classifying similar plant images. The contributions of this thesis are the explored different CNN models and improved performance of those models by designing and implementing the generalist-specialist CNN models for classifying similar plant images.en_US
dc.description.sponsorship"I would ... like to thank Mitacs, George Weston Ltd, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Faculty of Graduate Studies for their financial support of this work."en_US
dc.language.isoenen_US
dc.publisherUniversity of Winnipegen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPlant identificationen_US
dc.subjectDeep convolutional neural networks (CNN)en_US
dc.subjectHierarchical convolutional neural networks (HCNN)en_US
dc.subjectDigital agricultureen_US
dc.subjectPlant imagesen_US
dc.titleExploring Deep Neural Networks for Plant Image Classificationen_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.202205031043en_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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