PlantGuard: intelligent plant disease detection

dc.contributor.advisorHossain, Muhammad Iqbal
dc.contributor.authorKhanom, Nazifa
dc.date.accessioned2024-09-09T06:23:44Z
dc.date.available2024-09-09T06:23:44Z
dc.date.issued2024-05
dc.descriptionCataloged from the PDF version of the project report.
dc.descriptionIncludes bibliographical references (page 38).
dc.descriptionThis project report is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2024.
dc.description.abstractEvery year, there is significant crop loss in developing countries due to delays in identifying plant diseases. Prompt and accurate identification of these diseases, with less reliance on field experts, could greatly mitigate this issue. Recognizing plant diseases correctly, particularly when they present similar leaf textures, poses a significant challenge. It’s crucial to consider factors such as leaf color and various texture features to accurately predict plant defects. The objective of this project is to employ Deep Learning methodologies for the detection of plant diseases based on leaf images. Deep learning, specifically Convolutional Neural Networks, is chosen due to its effectiveness in extracting features from plant leaves, making it well-suited for image data analysis in this context.
dc.identifier.otherID 24173004
dc.identifier.otherhttps://dspace.bracu.ac.bd/server/api/core/items/2a12e607-da6b-45ac-a06a-9d9670e37067
dc.identifier.urihttp://hdl.handle.net/10361/24033
dc.language.isoen
dc.publisherBrac University
dc.sourceBRAC University Institutional Repository
dc.subjectLeaf textures
dc.subjectTexture features
dc.subjectCNN
dc.subjectConvolutional neural network
dc.subjectImage data analysis
dc.subjectDisease detection
dc.subjectDeep learning
dc.titlePlantGuard: intelligent plant disease detection
dc.typeProject Report

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