An Automated Tomato Quality Grading using Clustering based Support Vector Machine
This paper focus on representing the
technique of fruit grade classification, automated machine
vision based technology has become more potential and
important to many areas like agricultural sector and food
processing industry. The proposed system which calculates
the grade of fruit based on its external features. Grading of
fruits is one of the most important processes in post
harvesting, but this procedure is mostly carried out
manually which is not efficient as it requires enormous
number of employment, and tends to human error. The
grading process is carried out by capturing the fruit image
using digital camera and this image is interpreted using
image various processing techniques. Color is very
prominent feature for recognizing defect and ripeness of
the fruit. The major objective is to check the fruit quality
with high speed for analyzing maximum number of fruits
in least amount of time. The conventional process of fruit
quality assessment needs new tools deciding the quality of
fruit. This system performs color features and size of fruit
and captures the fruit side view image. The Otsu
thresholding and K-Means clustering algorithms are used
to extract the features of fruit. This system achieve the
fruit quality sorting using Support Vector Machine and
gives a few advantages over conventional techniques. This
system will help in the advancement of an automated non
destructive grading system with high speed, high accuracy
and low cost. Implementation of this system will have
applications in fruit quality grading in field like food
science and trades where standardization is essential
KeyWords
Tomato grading Support vector machine
Computer vision Classification Clustering
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