Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3159
Title: BlossomSnap: A Single Platform for all Anthurium Planters Based on The Sri Lankan Market
Authors: Rathnayake, R.M.S.T
Tharika Pramodi, M.L.A.D.
Gayathree, I. R
Rashmika, L.K.R
Gamage, M
Gamage, A
Keywords: chatbot
deep learning
diagnosis
extensively
image processing
machine learning
natural language processing
numerous
substantial
Issue Date: 15-Oct-2022
Publisher: Institute of Electrical and Electronics Engineers
Citation: R. M. S. T. Rathnayake, M. L. A. D. Tharika Pramodi, I. R. Gayathree, L. K. R. Rashmika, M. Gamage and A. Gamage, "BlossomSnap: A Single Platform for all Anthurium Planters Based on The Sri Lankan Market," 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0135-0141, doi: 10.1109/IEMCON56893.2022.9946542.
Series/Report no.: 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022;Pages 135 - 141
Abstract: The popular and extensively grown flowering plant known as the Anthurium is prized for its beauty. In Sri Lanka, anthuriums have a substantial international market. Although it is a significant field that can be further developed by expanding the market, but it has led to a lack of attention, resources, and a moderate cost of production, as well as from the absence of an appropriate market channel, all of which have led to lower productivity and quality. As a result, Anthurium growers have numerous challenges both in terms of production and marketing. This paper introduces a novel mobile application 'BlossomSnap' which involves automating and significantly enhancing the outdated manual process. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the diseases, pests, varieties, and the highest quality plants to create a more secure growing environment. It will provide high-quality, cost effective, and timely services. The first step of anthurium plant disease and pest diagnosis is carried out using image processing, deep learning, and machine learning technologies. In order to identify the infection stage, the following steps involve extracting, classifying, and detecting images of Anthurium flowers and leaves. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the proposed system. 'BlossomSnap' achieves an average accuracy of more than 80% and produces a better overall result. An in-place chatbot technology is intended to assist new planters with their problems. The Anthurium plant variety and quality detection methodology is used in concert with to determine the optimum market opportunity.
URI: https://rda.sliit.lk/handle/123456789/3159
ISBN: 978-166546316-4
Appears in Collections:Department of Information Technology

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