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DC Field | Value | Language |
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dc.contributor.author | Panchal, Bhaumik | - |
dc.date.accessioned | 2024-08-01T08:33:58Z | - |
dc.date.available | 2024-08-01T08:33:58Z | - |
dc.date.issued | 2024-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/12425 | - |
dc.description.abstract | Artificial 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.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 22MCEC06; | - |
dc.subject | Computer 2022 | en_US |
dc.subject | Project Report | en_US |
dc.subject | Project Report 2022 | en_US |
dc.subject | Computer Project Report | en_US |
dc.subject | 22MCE | en_US |
dc.subject | 22MCEC | en_US |
dc.subject | 22MCEC06 | en_US |
dc.title | Disease Identification and Prediction in Maize Plant | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, CE |
Files in This Item:
File | Description | Size | Format | |
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22MCEC06.pdf | 22MCEC06 | 5.66 MB | Adobe PDF | View/Open |
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