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dc.contributor.authorSsemambya, Orian
dc.date.accessioned2022-05-18T11:44:47Z
dc.date.available2022-05-18T11:44:47Z
dc.date.issued2022-02-14
dc.identifier.citationSsemambya, Orian. (2022). Severity Classification of Brown spot Disease in Passion Fruits Using Multi-task Deep Learning. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12281/12704
dc.descriptionA project report Submitted to the Department of Electrical and Computer Engineering in partial fulfillment of the requirements for the award of the degree in Bachelor of Science in Computer Engineering of Makerere University.en_US
dc.description.abstractExperts in plant pathology especially in passion fruit plants are few across the country. From this scenario arises the need for a tool that will enable the farmers to carry out brown spot disease diagnosis and crop health monitoring for the farmers. This report presents the development of a multi-task deep learning model which can be used to detect brown spot disease and also perform severity classification of the disease. Labeled data was provided for use in the project. The dataset contains leaves infected with brown spot disease with there corresponding severity levels, that is, Level 1, Level 2, Level 3 and Level 4. An object detection model and classification model were trained on the dataset provided. We proposed and developed a multi-task deep learning model. The model was deployed on a smart phone application.en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectSeverity Classificationen_US
dc.subjectDisease in Passion Fruitsen_US
dc.subjectMulti-task Deep Learningen_US
dc.titleSeverity Classification of Brown spot Disease in Passion Fruits Using Multi-task Deep Learning.en_US
dc.typeThesisen_US


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