Automated Diagnosis and Classification of Coffee Plant Diseases Using Deep Learning

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dc.contributor.authorIsaac Arou Mayol
dc.contributor.authorWilson Nyumbe
dc.contributor.authorMomodou Jallow
dc.contributor.authorBrian Mung’oka Mutua
dc.date.accessioned2024-05-14T14:25:51Z
dc.date.available2024-05-14T14:25:51Z
dc.date.issued2023-05-08
dc.description.abstractThis project addresses the critical challenge faced by coffee farmers in the early detection and management of diseases affecting coffee plants. Late identification often leads to significant crop loss, exacerbating poverty and food insecurity among farmers. The project: the Automated Diagnosis and Classification of Coffee Plant Diseases Using Deep Learning, provided an innovative solution. This solution utilizes a mobile application equipped with a trained deep learning model to enable farmers to detect diseases in their coffee crops early on. Through the use of image recognition technology, the application, powered by a deep learning model of 95.83% accuracy score, is able to accurately identify and classify diseases, empowering farmers to take timely and appropriate measures to prevent the spread of diseases and minimize crop loss. The project helps mitigate economic loss, promote responsible pesticide use, and improve the livelihoods of coffee farmers. By leveraging technology to address a pressing agricultural issue, this project contributes to sustainable farming practices and food security in coffee-producing regions.
dc.identifier.urihttps://hdl.handle.net/20.500.12311/1423
dc.language.isoen
dc.publisherUganda Christian University
dc.titleAutomated Diagnosis and Classification of Coffee Plant Diseases Using Deep Learning
dc.typeProject Report

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Bachelor of Science in Computer Science