Please use this identifier to cite or link to this item:
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14922
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chinnaiyan, R | - |
dc.contributor.author | Prasad, Ganesh | - |
dc.contributor.author | Sabarmathi, G | - |
dc.contributor.author | Swarnamugi | - |
dc.contributor.author | Balachandar, S | - |
dc.contributor.author | Divya, R | - |
dc.date.accessioned | 2024-03-30T10:10:58Z | - |
dc.date.available | 2024-03-30T10:10:58Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Vol. 370; pp. 577-590 | en_US |
dc.identifier.isbn | 9.78982E+12 | - |
dc.identifier.issn | 2190-3018 | - |
dc.identifier.uri | https://doi.org/10.1007/978-981-99-6702-5_47 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14922 | - |
dc.description.abstract | After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Smart Innovation, Systems and Technologies | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Early Blight | en_US |
dc.subject | Late Blight | en_US |
dc.subject | Potato Disease | en_US |
dc.subject | Potato Plant | en_US |
dc.title | Deep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Disease | 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.