AFDGA Defect Detection and Classification of Apple Fruit Imagesusing the Modified Watershed Segmentation Method using matlab


ABSTRACT:

Automatic inspection of fruits is the subject of many grading and sorting systems to decrease production costs and increase the quality of the production in the agro-industry. The main objective of this paper is to detect the defected fruits using AFDGA-[Apple Fruit Detection, Grading and Analyzation] approach, where it uses Modified Watershed Segmentation to segment the defection and analyze the Fruits using GLCM based feature extraction method, and finally classify the images by SRC in terms of the its features. Statistics, Textural and some geometrical features are utilized to classify the apple fruits and grade it. The experimental results show that our approach effectively identifies the defects and grade the apples accurately.  

EXISTING SYSTEM:

In Existing system,  boundary detection method is used to segment defect areas in citrus based on Sobel gradient mask, and then boundaries of objects of interest were identified using neighborhood and gradient thresholds. 

DISADVANTAGES:

It is difficult to set appropriate thresholds and producing continuous, one-pixel-wide contours.
PROPOSED SYSTEM:

     Our proposed system is to detect the defected fruits using Machine Learning algorithm. Fuzzy Segmentation to segment the defection and analyze the Fruits using GLCM based feature extraction method, and finally classify the images by SRC in terms of  its features. Statistics, Textural and some geometrical features are utilized to classify the apple fruits and grade it.

ADVANTAGES:

Classification accuracy is more.
FLOW DIAGRAM:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk                       : 20 GB HD

SOFTWARE REQUIREMENTS

Operating System             : Windows 7 
Software Programming Package : Matlab R2014a

REFERENCES:

Narendra V, G Hareesh K S,” Quality Inspection and Grading of Agricultural and Food Products by Computer Vision- A Review”, International Journal of Computer Applications, Volume 2 – No.1, May 2010.