dc.contributor.author | Eteku, Moses | |
dc.date.accessioned | 2023-01-20T12:07:28Z | |
dc.date.available | 2023-01-20T12:07:28Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Eteku, Moses. (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 (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda.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.uri | http://hdl.handle.net/20.500.12281/14589 | |
dc.description | A final year project report submitted to the College of Engineering Design and Art in partial fulfillment of the requirement for the award of the degree of Bachelor of Science in Computer Engineering of Makerere University. | en_US |
dc.description.abstract | Experts 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 brownspot 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 multitask
deep learning model. The model was deployed on a smart phone application. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Makerere University | en_US |
dc.subject | brownspot | en_US |
dc.subject | deep learning | en_US |
dc.subject | passion fruits | en_US |
dc.title | Severity Classification of Brown spot Disease in Passion Fruits Using Multi-Task Deep Learning | en_US |
dc.type | Thesis | en_US |