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dc.contributor.authorAshish Kumar, Rajbanshi
dc.date.accessioned2024-09-17T10:42:32Z
dc.date.available2024-09-17T10:42:32Z
dc.date.issued2023-05
dc.identifier.urihttp://10.10.11.6/handle/1/18066
dc.descriptionSCHOOL OF COMPUTING SCIENCE AND ENGINEERING DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING GALGOTIAS UNIVERSITY, GREATER NOIDA INDIAen_US
dc.description.abstractBecause crop species, crop disease symptoms, and environmental conditions vary, it might be difficult to detect potato leaf disease in its early stages. These elements make it challenging to spot potato leaf diseases in their early stages. To identify illnesses in potato leaves, numerous machine learning methods have been developed. The existing technology, however, is unable to identify crop species or crop diseases in general because these models are developed and evaluated using images of plant leaves from a particular region. A multi-level deep learning model for identifying potato leaf disease has been built in this study. Using the YOLOv5 image segmentation method, the first level of the algorithm extracts the potato leaves from the image of the potato plant.en_US
dc.language.isoen_USen_US
dc.publisherGalgotias Universityen_US
dc.subjectPotato Leafen_US
dc.subjectDisease Recognitionen_US
dc.titlePotato Leaf Disease Recognitionen_US
dc.typeTechnical Reporten_US


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