Analysis of Rice Analysis

Published on April 2017 | Categories: Documents | Downloads: 46 | Comments: 0 | Views: 459
of 3
Download PDF   Embed   Report

Comments

Content

Analysis of rice granules using image
processing and neural network
ABSTRACT:
In food handling industry, grading of
granular food materials is necessary
because samples of material are subjected
to adulteration. In the past, food products
in the form of particles or granules were
passed through sieves or other mechanical
means for grading purposes. In this paper,
analysis is performed on basmati rice
granules; to evaluate the performance
using image processing and Neural
Network is implemented based on the
features extracted from rice granules for
classification grades of granules. Digital
imaging is recognized as an efficient
technique, to extract the features from rice
granules in a non-contact manner. Images
are acquired for rice using camera.
Conversion to gray scale, Median
smoothing, Adaptive thresholding, Canny
edge detection, Sobel edge Detection,
morphological operations, extraction of
quantitative information are the checks
that are performed on the acquired image
using image processing technique through
Open source Computer Vision (Open CV)
which is a library of functions that aids
image processing in real time.

The morphological features acquired from
the image are given to Neural Network.
This work has been done to identify the
relevant quality category for a given rice
sample based on its parameters. The
performance of image processing reduced
the time of operation and improved the
crop recognition greatly. Grading results
obtained from Neural Network system
shows greater accuracy when compared
with the outputs from human experts.

Image Acquisition and
Smoothing
The first step in image processing is Image
Acquisition. Acquirement of an image is done
by using Nikon camera beneath homogeneous
lighting arrangement. Customary measures are
applied for improving the value of an image
through Pre-processing techniques. In this
paper, smoothing is done using Median Filters.

(a): Acquired image

Analysis of rice granules using image
processing and neural network
Candy Edge detection:
This is based on recognition of edges by
diverse edge operators. Discontinuities in
colour, grey level, texture, etc. are detected by
edge operators. Canny edge detector is a most
favourable detector which gives finest filtered
image. The gray scale image boundaries are
detected by this optimal recognition process.

(b):smoothning image

Thresholding : The subsequent step is to
segment an image which is one of the
imperative stages in image analysis. The
accurateness of this action is greatly reliant on
consequently extracted records. Thresholding
is a practice based on assimilation of light in
their surfaces to exemplify the regions of the
image. Threshold is to detach the regions in an
image with reverence to the stuffs, which is to
be analyzed.

(c): thresholding image

(d): candy edge detection

Feature Extraction:
Extraction of quantitative information from
segmented images is dealt with Feature
Extraction. Object identification and
classifications are performed based on diverse
algorithms of morphological features. The
features which were extracted from images of
rice kernels are Perimeter, Area, Minor-axis
Length and Major-axis Length using Contour
detection

Analysis of rice granules using image
processing and neural network
(e): contour image

Sponsor Documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close