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# Grape Detection in Vineyards Ishay Levi Eran Brill - PowerPoint PPT Presentation

Grape Detection in Vineyards Ishay Levi Eran Brill. Introduction.

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### Grape Detection in Vineyards Ishay LeviEran Brill

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:

• RGB values

• General brightness

Areas that contain grapes have:

• High, but not an extreme, gradient magnitude

• Average brightness

• Round edges

Areas that don’t contain grapes have:

• Extreme brightness values

• Non green RGB values

Grapes have gradient magnitude between 2 and 35.

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:

• edge detection

• Counting each edge point as an edge of a circle (using a pre fixed limits for radius, and all angles).

• Finding for each circle center its most used radius.

• For each found circle (that passes a certain limit of edge pointers), report all edge points in the picture that defines its edge.

• Return only the reported edges from previous section.

A simple input:

sample edge map using Hough

• A real Grape area

Sample edge detection using Hough

Errors can accrue- i.e. a grape can have a small brown pixel.

Reducing and correcting errors:

• using a window instead of single pixel.

• Using correcting function (finding “remains” and deleting them, filling small “holes” in grape area and with original picture.

• Using correct statistical analysis removes high percentage of non- grape areas. Most grape areas are left untouched.

• As getting the correct values for the variables of this usage. Big database of samples is required

• This information can be adjusted to suit specific conditions: season, time in day, type and age of grapes – improving the analysis and results!

• Using Hough transform improves results but cannot “stand alone”, Many areas in the images contain many circular-like shapes.

• In addition, its success relays on good edge detection, and correct radiuses input (as checking a large range cost running time, and not may causes errors).

• As in previous part, a good pre- analysis proves is the key for success.