Please use this identifier to cite or link to this item: http://repository.kln.ac.lk/handle/123456789/18382
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dc.contributor.authorDe Silva, R.-
dc.contributor.authorSenanayake, P. A.-
dc.date.accessioned2017-12-05T05:18:47Z-
dc.date.available2017-12-05T05:18:47Z-
dc.date.issued2017-
dc.identifier.citationDe Silva, R., and Senanayake, P. A. (2017). Identification of Water Stress of Plants Using Image Processing. 8th International Conference on Business & Information ICBI – 2017, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka. p.37.en_US
dc.identifier.urihttp://repository.kln.ac.lk/handle/123456789/18382-
dc.description.abstractLow Leaf Water Content (LWC) levels in plants lead to water stress, and the leaves become wilted. Most farmers use slight wilting as the visual indicator to water their plants. Though the researchers have studied LWC of plants extensively using remote sensing and complex methods, the agricultural industry cannot use them as they are complex and costly. This paper presents a simple method of recognizing water stress of leaves using leaf images taken by a smartphone. Our initial experiment on a large sample of mung bean leaves indicates that RGB values of images are related to water stress. The method would be beneficial to the agricultural industry as once further developed; it could be used to determine the watering time point of plants. The process can be automated by capturing the images by a camera mounted on a land or an aerial robot and processing them online.en_US
dc.language.isoenen_US
dc.publisher8th International Conference on Business & Information ICBI – 2017, Faculty of Commerce and Management Studies, University of Kelaniya, Sri Lanka.en_US
dc.subjectImage Processingen_US
dc.subjectLeaf Water Stress Recognitionen_US
dc.subjectLWCen_US
dc.subjectRGB Decompositionen_US
dc.titleIdentification of Water Stress of Plants Using Image Processing.en_US
dc.typeArticleen_US
Appears in Collections:ICBI 2017

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