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dc.contributor.authorAgora, Paul
dc.date.accessioned2024-01-19T06:36:17Z
dc.date.available2024-01-19T06:36:17Z
dc.date.issued2022-08-18
dc.identifier.citationAgora, Paul. (2022). An embedded, machine learning-enabled platform for in-field screening of plant disease and pest damage. (Unpublished undergraduate dissertation) Makerere University; Kampala, Uganda.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12281/18316
dc.descriptionA research report submitted to the College of Engineering Design and Art in partial fulfillment of the requirement for the award of the degree Bachelor of Telecommunications Engineering of Makerere University.en_US
dc.description.abstractThis report contains the details of the project that we did for a span of 8 months. Chapter one covers the project background, problem statement, justification and objectives of the project Chapter two covers the literature review of the modules we covered and these include; Machine learning course basics, deep learning, setting up development environments, making machine learning models and related work Chapter three covers the practical work including the methodology of the project that we used Finally, chapter four contains my new findings, challenges, recommendations and conclusions.en_US
dc.language.isoenen_US
dc.publisherMakerere Universityen_US
dc.subjectMachine learning-enabled platformen_US
dc.subjectIn-field screening of plant diseaseen_US
dc.subjectIn-field screening of pest damageen_US
dc.titleAn embedded, machine learning-enabled platform for in-field screening of plant disease and pest damageen_US
dc.typeThesisen_US


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