Embedded System for Identifying the Quality of Grass Using Colour Patterns for the Sri Lankan Dairy Industry
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Date
2020
Journal Title
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Volume Title
Publisher
Uva Wellassa University of Sri Lanka
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
Description
Keywords
Animal Sciences, Computer Science, Information Science, Computing and Information Management, Dairy Industry