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Riceguardnet: custom cnns for precise bacterial and fungal infection classification

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dc.contributor.author Katuwawala, NKAC
dc.contributor.author Kumarasinghe, KMSJ
dc.contributor.author Rajapaksha, RMIK
dc.contributor.author Rathnayaka, DMGD
dc.contributor.editor Piyatilake, ITS
dc.contributor.editor Thalagala, PD
dc.contributor.editor Ganegoda, GU
dc.contributor.editor Thanuja, ALARR
dc.contributor.editor Dharmarathna, P
dc.date.accessioned 2024-02-06T08:41:50Z
dc.date.available 2024-02-06T08:41:50Z
dc.date.issued 2023-12-07
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22195
dc.description.abstract Rice cultivation is a vital component of many nations’ agricultural landscapes, often relying on traditional knowledge passed down through generations. However, disease identification in rice crops presents challenges, as many diseases are difficult to discern through visual inspection alone. This leads to delayed or inaccurate diagnoses, placing entire plantations at risk and discouraging new entrants to the field. This research addresses the pressing issue of timely and accurate disease identification in rice plants, focusing on three common diseases: Bacterial Leaf Blight, Brown Spot, and Leaf Smut, which are caused by bacteria and fungi. These diseases can proliferate rapidly, making early detection crucial. A custom Convolutional Neural Network (CNN) model was developed and trained using a dataset comprising 16,000 images, with 4,000 images for each disease and a healthy class. The model achieved an impressive accuracy of 99.87% on the test dataset, demonstrating its effectiveness in disease classification. This innovative approach provides a solution to the challenges faced by rice farmers, enabling quick and accurate disease identification. The research findings hold significant promise for improving rice cultivation practices, reducing the risk of crop loss, and encouraging new entrants into the field of rice farming. en_US
dc.language.iso en en_US
dc.publisher Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.subject Convolutional neural networks (CNNs) en_US
dc.subject Image processing Image classification en_US
dc.subject Plant disease diagnosis en_US
dc.subject Data augmentation en_US
dc.title Riceguardnet: custom cnns for precise bacterial and fungal infection classification en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty IT en_US
dc.identifier.department Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa. en_US
dc.identifier.year 2023 en_US
dc.identifier.conference 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.place Moratuwa, Sri Lanka en_US
dc.identifier.pgnos pp. 1-6 en_US
dc.identifier.proceeding Proceedings of the 8th International Conference in Information Technology Research 2023 en_US
dc.identifier.email ayoma.18@itfac.mrt.ac.lk en_US
dc.identifier.email sashikaj@uom.lk en_US
dc.identifier.email inoshi.18@itfac.mrt.ac.lk en_US
dc.identifier.email geethma.18@itfac.mrt.ac.lk en_US


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  • ICITR - 2023 [47]
    International Conference on Information Technology Research (ICITR)

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