Development Of Automatic Maize Quality Assessment System Using Image Processing Techniques
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Date
2015-11
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Addis Ababa University
Abstract
Maize is a very important crop where its circulation in the market has to conform to the
rules of quality inspection. Currently, maize sample quality inspection is performed
manually by human experts through visual evaluation and the constituents will be
classified into foreign matter, rotten and diseased, healthy, broken, discolored, shriveled
and pest damaged kernels. However, visual evaluation requires significant amount of
time, trained and experienced people. Besides, it is affected by bias and inconsistencies
associated with human nature. Such approach will not be satisfactory for large scale
inspection and grading unless fully automated.
The goal of this research work is to develop a system capable of assessing the quality of
maize sample constituents using digital image processing techniques and artificial
neural network classifier based on the standard for maize set by the Ethiopian
Standards Agency. A novel segmentation technique is proposed to segment and lay the
foundation for feature extraction. A total of 24 features (14 color, 8 shape and 2 size)
have been identified to model maize sample constituents.
For classificat ion of maize samples, a feedforward artificial neural network classifier
with backpropagat ion learning algorithm, 24 input and 7 output nodes, corresponding
to the number of features and classes respectively has been designed. The network is
trained and its performance is compared against other classifiers both empirically and
based on supporting facts from the literature. For the purpose of training the classifier, a
total of 534 kernels and foreign matters have been collected from Ethiopian Grain Trade
Enterprise. The training data is randomly apportioned into training (70%) and testing
(30%). The classifier achieved an overall classification accuracy of 97.8%. The success
rates for detecting foreign, rotten and diseased, healthy, broken, discolored, shriveled
and pest damaged kernels are 100%, 95.2%, 98.6%, 98.8%, 100%, 98.4%, and 94.8%,
respectively.
Keywords: Artificial neural network, Maize quality assessment, Reconstructed image,
Merged image, Color image segmentation, Digital image processing, Color structure tensor
Description
Keywords
Artificial Neural Network; Maize Quality Assessment; Reconstructed Image; Merged Image; Color Image Segmentation; Digital Image Processing; Color Structure Tensor