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dc.contributor.authorKiyegga, Raymond Paul
dc.date.accessioned2021-09-08T10:47:53Z
dc.date.available2021-09-08T10:47:53Z
dc.date.issued2020-06
dc.identifier.urihttp://hdl.handle.net/11071/12134
dc.descriptionA Thesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technologyen_US
dc.description.abstractDetermination of disease and nutrients in plants is still a new concept. Despite efforts from researchers to come up with improved techniques of detecting diseases and nutrients, many have been limited to only specific plant images and no other data such as weather, surrounding conditions to back up the decision. Plant disease identification is very crucial to food production and security, however current practices in Africa include visual identification and microscopy. Visual methods are greatly affected by cognitive error while microscopy is time consuming. It is difficult to detect plant disease unless one is guided by expert knowledge. Therefore, there is a need to apply machine learning techniques to make use of this expert knowledge. Current practices include use of spectral images to achieve this in fruits and other applications to help farmers without access to this knowledge to diagnose plant diseases. One notable challenge is determining nutrient content using images. Current applications require a farmer to look at the provided image and compare with what he sees on the plant. This research work proposes a machine learning model that can automatically detect the disease affecting a tomato plant as well as the nutrition level in the plant leaves. The farmer captures an image on their phone while in the plantation, based on the features from the leaf, the model analyses the image and returns the details of the classification in terms of type of disease and presence of deficiency of nutrients. The model was built on convolution neural network and achieved an accuracy of 85% using a learning rate of 0.001. It trained on 8000 samples using 30 epochs. The model was trained, validated and tested.en_US
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDeficiencyen_US
dc.subjectNeural networksen_US
dc.titleA Computer vision based model for tomato plant nutrient and disease classificationen_US
dc.typeThesisen_US


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