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Grape Detection in Vineyards Ishay Levi Eran Brill. Introduction.

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Grape detection in vineyards ishay levi eran brill

Grape Detection in Vineyards Ishay LeviEran Brill


Introduction
Introduction

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.


Statistic analysis
Statistic analysis

The information was analyzed based not on a specific pixel but on a selected window, we examine:

  • Gradient size

  • RGB values

  • General brightness


Statistic analysis finding
Statistic analysis - finding

Areas that contain grapes have:

  • High, but not an extreme, gradient magnitude

  • Average brightness

  • Round edges

    Areas that don’t contain grapes have:

  • Extreme gradient values

  • Extreme brightness values

  • Non green RGB values


Gradient
Gradient

Grapes have gradient magnitude between 2 and 35.

Average gradient = 1.8

Average gradient = 7.4

Average gradient = 0.9


Rgb values
RGB values

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


Shape recognition
Shape recognition

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.


Using hough transform
Using Hough transform

A veryshort reminder:

Using Hough for detecting circles


Using hough transform1
Using Hough transform

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.


Hough transform some examples
Hough transform – some examples

A simple input:

sample edge map using Hough


Hough transform some examples1
Hough transform – some examples

  • A real Grape area

    Sample edge detection using Hough









Correcting errors
Correcting errors

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.


Conclusion
conclusion

  • 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!


Conclusion1
conclusion

  • 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.


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