Segmentation using fuzzy cluster-based thresholding method for apple fruit sorting

Apple fruit sorting has been an important postharvest process carried on for the sorting of diseased apple fruits. A fuzzy cluster-based thresholding (FCBT) method for segmenting the region of interest from an apple image has been proposed for sorting apples in this study. As the first step, the acq...

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Bibliographic Details
Published in:IET image processing 2020-12, Vol.14 (16), p.4178-4187
Main Authors: Henila, Manickam, Chithra, Palaniappan
Format: Article
Language:English
Subjects:
acquired RGB colour image
agricultural products
apple fruit sorting
apple image
computer vision
different fuzzy cluster bins
diseased apple fruits
diseases
FCBT image
FCBT method
FCBT value
feature extraction
fuzzy cluster‐based thresholding method
fuzzy set theory
greyscale image
image classification
image colour analysis
image segmentation
important postharvest process
Kapur's methods
neural nets
pattern clustering
Research Article
segmented images
similar greyscale thresholding methods
sorting apples
threshold value
visual segmentation accuracy
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Summary:Apple fruit sorting has been an important postharvest process carried on for the sorting of diseased apple fruits. A fuzzy cluster-based thresholding (FCBT) method for segmenting the region of interest from an apple image has been proposed for sorting apples in this study. As the first step, the acquired RGB colour image of an apple fruit was converted into a greyscale image. Then, five different fuzzy cluster bins with overlapped pixel ranges were taken and greypixel values were binned into them. A cluster with the maximum number of pixels was selected for calculating the threshold value. The region of interest from the apple image was then segmented using the proposed FCBT value. Features extracted from the segmented images were given as input to a fully-connected deep neural network for a classification. The performance of the FCBT method was compared with similar greyscale thresholding methods like Otsu's and Kapur's methods. The visual segmentation accuracy and the execution speed showed that the FCBT outperformed the other methods in segmenting the diseased area. A fully-connected deep neural network model with the FCBT image extracted features as input values gave a 98.33% accuracy rate in sorting the apple images.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2020.0705