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

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dc.contributor.author Jayaweera, S.M.D.B.
dc.contributor.author Rupasinghe, P.M.S.
dc.contributor.author Eranda, S.A.L.
dc.contributor.author Ratnayake, A.M.B.
dc.contributor.author Jayasinghe, J.M.P.
dc.contributor.author Wilson, R.S.I.
dc.date.accessioned 2021-02-01T08:19:50Z
dc.date.available 2021-02-01T08:19:50Z
dc.date.issued 2020
dc.identifier.isbn 9789550481293
dc.identifier.uri http://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/5725/proceeding_oct_08-202.pdf?sequence=1&isAllowed=y
dc.description.abstract Sri 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 identification en_US
dc.language.iso en en_US
dc.publisher Uva Wellassa University of Sri Lanka en_US
dc.relation.ispartofseries ;International Research Conference
dc.subject Animal Sciences en_US
dc.subject Computer Science en_US
dc.subject Information Science en_US
dc.subject Computing and Information Management en_US
dc.subject Dairy Industry en_US
dc.title Embedded System for Identifying the Quality of Grass Using Colour Patterns for the Sri Lankan Dairy Industry en_US
dc.title.alternative International Research Conference 2020 en_US
dc.type Other en_US

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