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dc.contributor.authorKerre, Deperias Webula
dc.date.accessioned2022-03-23T08:01:26Z
dc.date.available2022-03-23T08:01:26Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/11071/12753
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.abstractStrawberry is one of the cash crops that are being grown in Kenya for both export and local consumption. However, strawberry fungal leaf diseases are threatening the existence of this crop which is an important input in the agricultural production sector. The types of strawberry fungal leaf diseases resulting to greater losses in production include Strawberry Leaf scorch, Strawberry Leaf Spot and Strawberry Leaf Blight. The biggest challenge the farmers face is that of correctly classifying these diseases based on observable leaf features. Famers have incurred losses due to poor/incorrect control measures which results from the misdiagnosis of these diseases. This scenario is more pronounced in rural settings where the farmers have a limited access to expertise in modern agricultural production. As a result of this, automated classification of strawberry plant fungal leaf diseases is highly desired. The literature review found several computer vision techniques that have been leveraged in Strawberry fungal leaf disease detection. Among these solutions are the convolutional neural network-based models. Despite the high detection accuracy, the models do not cover another of strawberry fungal leaf diseases such as leaf spot and fail to generalize well on unseen data. The models also do not consider cases where more than one disease occur on the same part of the plant, in this case the leaf. In this study, a deep learning model was proposed for classifying fungal leaf diseases in strawberry based on an experimental research design. The model generalized well on previously unseen data and considered a scenario where multiple diseases occur on the same leaf (Leaf Scorch and Leaf Blight). The model also covered strawberry Leaf Spot that was not covered by any of the existing deep learning models. Data samples containing a total of 1,134 leaf images, categorized into five classes including healthy leaf images were split into 80% training and 20% validation. The disease classes include strawberry leaf spot, leaf scorch, leaf blight and a class where two diseases (Leaf Blight and leaf Scorch) occur together. The model was trained on 30 epochs from scratch with batch normalization implemented within the convolutions in Keras framework and validated using a confusion matrix. The model achieved an outstanding classification accuracy of 98%, precision of 97% , recall of 95.7% and an F1-score of 96.3%.en_US
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectBatch Normalization (BN)en_US
dc.subjectComputer Vision(CV)en_US
dc.subjectConvolutional Neural Networks (CNNs)en_US
dc.subjectData augmentation (DA)en_US
dc.subjectDeep learningen_US
dc.subjectImage processingen_US
dc.subjectStrawberry fungal leaf disease detectionen_US
dc.titleA Deep normalized neural network model for strawberry fungal leaf disease detectionen_US
dc.typeThesisen_US


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