Grape Detection in Vineyards Ishay Levi Eran Brill. Introduction.
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In our work we characterized areas that are likely to contain grapes as well as areas which don't. We characterized areas based on both statistic information we gathered from the database, and according to the shape their edges create.
The information was analyzed based not on a specific pixel but on a selected window, we examine:
Areas that contain grapes have:
Areas that don’t contain grapes have:
Grapes have gradient magnitude between 2 and 35.
Average gradient = 1.8
Average gradient = 7.4
Average gradient = 0.9
Generally in a green pixel G>R G>B
R =168 G =74 B =69
R =120 G =122 B =100
R =63 G =49 B =79
R =253 G =253 B =253
After cleaning most non- grape section using image statistical analysis.
Second phase: cleaning using shape recognition.
As grape areas contain mainly two shapes:
Circles and ellipses.
Will use a method learned during the course for shape recognition.
A veryshort reminder:
Using Hough for detecting circles
Brief description of usage:
A simple input:
sample edge map using Hough
Sample edge detection using Hough
Errors can accrue- i.e. a grape can have a small brown pixel.
Reducing and correcting errors: