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

Author(s):

A. Raihana , PSNA College of Engineering and Technology, Dindigul-624619; R. Sudha, PSNA College of Engineering and Technology, Dindigul-624619

Keywords:

Fruit Detection, Apple Fruit Analyzation, Feature Extraction, SVM Classifier

Abstract:

One of the commercial industry is Food industry, which utilizes image processing for investigating the product at the time of harvesting. Various imperfections on the fruit’s skin can help to analyze the quality of the fruit. Several agriculture based softwares were developed to find out the quality of the products using image processing techniques. 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 SVM in terms of the its features. Statistics, Textural and some geometrical features are utilized to classify the apple fruits and grade it. To improve the efficiency [detection and grading] it is confirmed the computer based AFDGA procedure is verified in FPGA environment. VLSI code is created and using the binary factors the Fruit is decided as Good fruit or Defected Fruit. The simulation results obtained from MATLAB and VLSI are compared for performance evaluation.

Other Details:

Manuscript Id	:	IJSTEV3I6054
Published in	:	Volume : 3, Issue : 6
Publication Date	:	01/01/2017
Page(s)	:	75-85

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