Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/973
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dc.contributor.authorVasanthan, N.-
dc.contributor.authorShimran, Mohamed-
dc.contributor.authorAhkam, A.-
dc.contributor.authorIshak, I.-
dc.contributor.authorSilva, C.-
dc.contributor.authorKuruppu, T.A.-
dc.date.accessioned2022-02-07T07:20:24Z-
dc.date.available2022-02-07T07:20:24Z-
dc.date.issued2021-12-09-
dc.identifier.issn978-1-6654-0862-2/21-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/973-
dc.description.abstractAgricultural productivity plays a vital role in contributing to a nation’s economy. Farmers nowadays are concerned due to disease persistence in crops and plants, and it also affects the economy indirectly, so it is important to come up with a solution to detect plant diseases and educate the farmers about the solutions to retaliate against the diseases. Proper care is mandatory to safeguard the quality of plants. The existing traditional methods consume a massive amount of time and resources hence, it’s costly. Due to the importance of continuous monitoring, it seems impractical for a farmer to implement the traditional methods on large scale. The Traditional systems which are used lack the ability to identify diseases out of their predefined scope. As a solution, we came up with an autolearning system that identifies new plant diseases and provides remedies. This paper showcases the image processing techniques to detect plant diseases, Auto ML techniques to create new models for plants and corresponding diseases, Diseases are identified using image processing, Remedies are extracted for the given plant diseases using unstructured data from web data crawling. The business intelligence model uses NLP to provide ideas about the trending plants and plantrelated diseases are also discussed in this paper.en_US
dc.description.sponsorshipCo-Sponsor:Institute of Electrical and Electronic Engineers (IEEE) Academic sponsor:SLIIT UNI Gold Sponsor :London Stock Exchange Group (LSEG)en_US
dc.language.isoenen_US
dc.publisher2021 3rd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.subjectContinuous Learningen_US
dc.subjectTwitteren_US
dc.subjectCNNen_US
dc.subjectAuto-Kerasen_US
dc.subjectAutoMLen_US
dc.subjectImage Classificationen_US
dc.subjectWeb Data Extractionen_US
dc.subjectBusiness Intelligenceen_US
dc.titleAuto Training an AI for Detecting Plant Disease Using Twitter Data Annexed With a Plant Anthologyen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC54203.2021.9671164en_US
Appears in Collections:3rd International Conference on Advancements in Computing (ICAC) | 2021
Department of Computer systems Engineering-Scopes
Research Papers - IEEE

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