Please use this identifier to cite or link to this item: http://10.1.7.192:80/jspui/handle/123456789/12425
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dc.contributor.authorPanchal, Bhaumik-
dc.date.accessioned2024-08-01T08:33:58Z-
dc.date.available2024-08-01T08:33:58Z-
dc.date.issued2024-06-01-
dc.identifier.urihttp://10.1.7.192:80/jspui/handle/123456789/12425-
dc.description.abstractArtificial intelligence (AI)’s deep learning branch has attracted a lot of interest from both academia and industry because of its capacity for autonomous learning and feature extraction, particularly in speech, natural language, picture, and video processing. With a focus on plant disease detection and pest evaluation, it has emerged as a crucial field of study for agricultural plant protection. Deep learning contributes to improving objectivity, breaking through the constraints of manually chosen illness features, and speeding up scientific and technical advancements. This study discusses state-of-the-art techniques and problems while reviewing the developments in deep learning for agricultural leaf disease identification. It attempts to be a useful tool for researchers studying plant diseases and pest identification, addressing important problems that must be solved in order to advance further.en_US
dc.publisherInstitute of Technologyen_US
dc.relation.ispartofseries22MCEC06;-
dc.subjectComputer 2022en_US
dc.subjectProject Reporten_US
dc.subjectProject Report 2022en_US
dc.subjectComputer Project Reporten_US
dc.subject22MCEen_US
dc.subject22MCECen_US
dc.subject22MCEC06en_US
dc.titleDisease Identification and Prediction in Maize Planten_US
dc.typeDissertationen_US
Appears in Collections:Dissertation, CE

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