Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2287
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | David, Hepzibah Elizabeth | - |
dc.contributor.author | Ramalakshmi, K | - |
dc.contributor.author | Gunasekaran, Hemalatha | - |
dc.contributor.author | Venkatesan, R | - |
dc.date.accessioned | 2023-12-09T08:56:05Z | - |
dc.date.available | 2023-12-09T08:56:05Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | pp. 274-278 | en_US |
dc.identifier.isbn | 9781665405201 | - |
dc.identifier.isbn | 9781665405218 | - |
dc.identifier.issn | 2575-7288 | - |
dc.identifier.issn | 2469-5556 | - |
dc.identifier.uri | https://doi.org/10.1109/ICACCS51430.2021.9441714 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2287 | - |
dc.description.abstract | Tomatoes are the most common vegetable crop widely cultivated in the agricultural fields in India. The tropical climate is ideal for its growth, however certain climatic conditions and various other factors affect the normal growth of tomato plants. Apart from these climatic conditions and natural disasters, plant disease is a major crisis in crop production and results in economic loss. The traditional disease detection methods for tomato crops could not produce the expected outcome and the detection period for diseases was slow. The early detection of diseases can give better results than the existing detection models. Thus, computer vision-based technology deep learning techniques could be implemented for earlier disease detection. This paper introduces a comprehensive analysis of the disease classification and detection techniques implied for tomato leaf disease identification. This paper also reviews the merits and drawbacks of the methodologies proposed. This paper finally proposes the early disease detection technique to identify tomato leaf disease using hybrid deep-learning architecture. © 2021 IEEE. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2021 7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021 | en_US |
dc.subject | CNN | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Hybrid CNN-RNN | en_US |
dc.subject | Leaf Disease Detection | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Tomato Leaf Disease | en_US |
dc.title | Literature Review of Disease Detection In Tomato Leaf Using Deep Learning Techniques | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.