Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3524
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dc.contributor.authorWanninayake, K.M.I.S-
dc.contributor.authorBambaranda, L.G.S. W-
dc.contributor.authorWickramaarachchi, T.I-
dc.contributor.authorPathirana, U.C.S.L-
dc.contributor.authorVidhanaarachchi, S-
dc.contributor.authorNanayakkara, A.A.E.-
dc.contributor.authorGunapala, K.R.D.-
dc.contributor.authorSarathchandra, S.R.-
dc.contributor.authorGamage, A.I-
dc.contributor.authorDe Silva, D.I-
dc.date.accessioned2023-08-09T10:11:06Z-
dc.date.available2023-08-09T10:11:06Z-
dc.date.issued2023-06-26-
dc.identifier.citationK. M. I. S. Wanninayake et al., "IoT-Enabled Smart Solution for Rice Disease Detection, Yield Prediction, and Remediation," 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Istanbul, Turkiye, 2023, pp. 1-9, doi: 10.1109/HORA58378.2023.10155770.en_US
dc.identifier.isbn979-8-3503-3752-5-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3524-
dc.description.abstractSri Lanka's rice cultivation is a vital industry supporting over 1.8 million cultivators and providing staple sustenance for 21.8 million people. According to Sri Lanka's Central Bank, rice cultivation contributed 2.7% to the country's GDP in 2020 [3]. Pests and diseases, particularly rice thrips damage and rice blast disease, are a challenge for the industry, as they cause yield loss. This paper describes an intelligent solution that aids stakeholders by detecting and classifying the disease, forecasting its dispersion, and providing remedies. The proposed solution is approached with deep learning techniques for real-time detection and classification of the disease, location tracking of infected areas, and pesticide application on the target. In addition, it predicts the spread of disease based on the locations of infected individuals. In addition, the solution enables Machine-learning algorithms to recommend appropriate rice varieties and predict yields. In controlled experiments utilizing data from Sri Lankan paddy fields, the proposed method obtained high accuracy rates of 89%-98% in identifying disease and rice varieties and yield prediction. This system has the potential to increase rice production and productivity, decrease yield loss, and benefit the Sri Lankan rice industry and producers.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA);-
dc.subjectRice Diseasesen_US
dc.subjectYield Predictionen_US
dc.subjectDisease identificationen_US
dc.subjectObject Detectionen_US
dc.subjectDisease Dispersionen_US
dc.subjectDeep Learningen_US
dc.titleIoT-Enabled Smart Solution for Rice Disease Detection, Yield Prediction, and Remediationen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/HORA58378.2023.10155770en_US
Appears in Collections:Department of Computer Science and Software Engineering
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE

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