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Title: Smart Plant Disorder Identification using Computer Vision Technology
Authors: Manoharan, S
Sariffodeen, B
Ramasinghe, K. T
Rajaratne, L. H
Kasthurirathna, D
Wijekoon, J. L
Keywords: Smart Plant Disorder
Disorder Identification
Computer Vision Technology
Issue Date: 4-Nov-2020
Publisher: IEEE
Citation: S. Manoharan, B. Sariffodeen, K. T. Ramasinghe, L. H. Rajaratne, D. Kasthurirathna and J. L. Wijekoon, "Smart Plant Disorder Identification using Computer Vision Technology," 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2020, pp. 0445-0451, doi: 10.1109/IEMCON51383.2020.9284919.
Series/Report no.: 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON);Pages 0445-0451
Abstract: The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.
ISSN: 2644-3163
Appears in Collections:Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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