This document describes a fruit detection technique using morphological image processing. It outlines image acquisition by collecting fruit sample images in JPEG format. Image preprocessing steps like enhancement and noise removal are applied. Color and texture features are then extracted using color space conversion and Canny edge detection. Image segmentation is performed using a clustering algorithm. Morphological dilation is applied to segmented images to count fruit objects. The results show this technique can automatically count and distinguish fruits, providing a low-cost alternative to manual quality inspection.

1. FRUIT DETECTION USING
MORPHOLOGICAL IMAGE
PROCESSING TECHNIQUE
Done By Ahmad Taweel
1

2. 1. Outline
• Introduction
• Image Acquisition
• Image Preprocessing
• Color Feature
• Texture Feature
• Result
• Conclusion
2

3. 2. Introduction
• Quality inspection
• Manual
• Not accurate
• Time consuming
• Expensive
• Fruit quality grading is mandatory condition
• New techniques in fruit quality assessment is necessary
3

4. 2. Introduction
• Locating fruit from tree made
• Harvesting more efficient
• Analysis more easier
• Food appearance  Evaluation of quality of fresh food
• Packinghouses demanded a system
• Detect fruit skin defects
4

5. 3.1 Image Acquisition
• Our first step here is to collect sample images of fruits
which are going to be decided the number of fruit present
on the tree.
• Store all the images in .Jpg format
• Resolution 429*322 pixels
5

6. 3.1 Image Acquisition
6

7. 3.2 Image Preprocessing
• The Aim of this process is to
• Improve image data
• Enhance some of its features
• Approaches:
• Image Enhancement
• Noise remove
7

8. 3.2.1 Image Enhancement
• Improve visibility
• Remove any flickering
• Improve contrast
• Approaches:
• Spatial domain
• Frequency domain
8

9. 3.2.2 Noise Remove
• To remove noise we use Masking
• Consist of:
• Filter mask
• Point to point
• Filter values are predefined
• Used:
• Classify
• Grade
9

10. 3.2 Image Preprocessing
10

11. 3.3 Feature Extraction
• Methods:
• Color Feature
• Color space conversion method
• Texture Feature
• Canny edge detection method
• Dilation method
11

12. 3.4 Image Segmentation
• Method:
• Segmentation Partition clustering algorithm
• K partition each partition represent a cluster
• Drawback
• Poor result when a point is close to the center of another cluster
• Overlapping
12

13. 3.4 Image Segmentation
13

14. 3.5 Canny Edge Detection
• What is Edges?
• Significant transitions in pixels
• Canny Edge Detection ?
• Multi-stage algorithm
• Subject to noise in the image
• How does it work?
• Remove the noise
• Canny Edge Detection
14

15. 3.5 Canny Edge Detection
15

16. 3.6 Morphology (Dilation)
• Morphological Image Processing relay only on the relative
ordering of pixel values, not on their numerical values
• Dilation adds pixels to the boundaries of objects in an
image
• The number of pixels added depends on the size and
shape of the structuring element
• Applied:
• Gray scale images
• Binary image
16

17. 3.6 Morphology (Dilation)
17

18. RESULT (1/2)
• Counting objects is done
• Manually
• By costly electronic systems
• Image Processing using MATLAB
• Effective
• Quick
• Low cost
• No costly equipment
18

19. RESULT (2/2)
• Accuracy depends on size of the disk structuring element
• Big objects  more counting accuracy
• Small objects  less counting accuracy
• Accuracy can be increased by separating conglutination
among the objects
19

20. 20

21. CONCLUSION (1/2)
• Quality inspection is done
• Manually
• highly inconsistence in accuracy
• Time consuming
• Boring
• Expensive
21

22. CONCLUSION (2/2)
• Image Processing algorithms
• Automatically count
• Distinguish fruits
• Packinghouses can use this system to guarantee quality
of products.
22

23. Thank You
23