Embedded System for Identifying the Quality of Grass Using Colour Patterns for the Sri Lankan Dairy Industry

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dc.contributor.authorJayaweera, S.M.D.B.
dc.contributor.authorRupasinghe, P.M.S.
dc.contributor.authorEranda, S.A.L.
dc.contributor.authorRatnayake, A.M.B.
dc.contributor.authorJayasinghe, J.M.P.
dc.contributor.authorWilson, R.S.I.
dc.date.accessioned2021-02-01T08:19:50Z
dc.date.available2021-02-01T08:19:50Z
dc.date.issued2020
dc.description.abstractSri Lankan dairy sector operates at its suboptimal level. Efficient and reliable technologies are needed to increase productivity enabling farmers to make farm management decisions based on accurate and current information. Precision farming technologies could be successfully integrated to monitor farm-grown pasture and make real-time decisions to optimize utilization. The present study is aimed to develop an embedded system-based method to efficiently monitor and utilize available pasture in dairy farming. A custom-made drone with F450 frame and Ardu pilot mega 2.6 was used in the study. The drone was tested at Uva Wellassa University and NLDB farm, Melsiripura. Flight controller was automated using the mission planner tool to fly at an automated waypoint flight of a Grid pattern. Drone mounted go-pro camera was used to acquire pre-processed images contained GPS metadata and webODM tool merged images with GPS data to produce a georeferenced output (Orthomosaic image). Developed shadow removal algorithm converted BGR to YCbCr color space and computed average Y channel and intensities. Subsequent process detected shadow regions and saved binary shadow images. Then the algorithm computed average pixel intensities of shadow and non-shadow areas adding difference with Y channel. Furthermore, the color identification algorithm obtained shadow processed image and applied the median filter (blur/Sharpened image) to convert color mode from RGB to HSV format. The image was color filtered based on identified color ranges of high yield grass. To identify overall color identification, an aerial map was marked by an expert in the field, subsequently algorithm processed image and marked image compared. Images were measured by pixels coverage of marked area and results provided a 90% identification rate through the algorithm. Results revealed, developed an embedded system-based method successfully measured field grass coverage compared with a manual method. Keywords: Embedded system, Pasture, Precision agriculture, Colour identificationen_US
dc.identifier.isbn9789550481293
dc.identifier.urihttp://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/5725/proceeding_oct_08-202.pdf?sequence=1&isAllowed=y
dc.language.isoenen_US
dc.publisherUva Wellassa University of Sri Lankaen_US
dc.relation.ispartofseries;International Research Conference
dc.subjectAnimal Sciencesen_US
dc.subjectComputer Scienceen_US
dc.subjectInformation Scienceen_US
dc.subjectComputing and Information Managementen_US
dc.subjectDairy Industryen_US
dc.titleEmbedded System for Identifying the Quality of Grass Using Colour Patterns for the Sri Lankan Dairy Industryen_US
dc.title.alternativeInternational Research Conference 2020en_US
dc.typeOtheren_US
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