Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14922
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dc.contributor.authorChinnaiyan, R-
dc.contributor.authorPrasad, Ganesh-
dc.contributor.authorSabarmathi, G-
dc.contributor.authorSwarnamugi-
dc.contributor.authorBalachandar, S-
dc.contributor.authorDivya, R-
dc.date.accessioned2024-03-30T10:10:58Z-
dc.date.available2024-03-30T10:10:58Z-
dc.date.issued2023-
dc.identifier.citationVol. 370; pp. 577-590en_US
dc.identifier.isbn9.78982E+12-
dc.identifier.issn2190-3018-
dc.identifier.urihttps://doi.org/10.1007/978-981-99-6702-5_47-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14922-
dc.description.abstractAfter 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.isoenen_US
dc.publisherSmart Innovation, Systems and Technologiesen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectEarly Blighten_US
dc.subjectLate Blighten_US
dc.subjectPotato Diseaseen_US
dc.subjectPotato Planten_US
dc.titleDeep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Diseaseen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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