Development of an Automated Grading System of White Pea Bean Using Image Processing Techniques Convergence with Ann
Mesfin Fekadu Abeza1, Sudhir Kumar Mohapatra2, Befekadu Belete3
1Mesfin Fekadu*, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
2Dr. Sudhir K., Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
3Befekadu Belete, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Manuscript received on September 12, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 664-670 | Volume-9 Issue-1, October 2019 | Retrieval Number: F8880088619/2019©BEIESP | DOI: 10.35940/ijeat.F8880.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: White pea bean is a very important crop where its circulation in the market has to conform to the rules of quality inspection. Currently, white pea bean sample quality inspection is performed manually by human experts through visual evaluation and the constituents 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. A total of 24 features (14 color, 8 shape and 2 size) have been identified to model white pea bean sample constituents. For classification of White pea bean samples, a feedforward artificial neural network classifier with backpropagation 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 using empirically. For the purpose of training the classifier, a total of 602 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 96.8%. The success rates for detecting foreign, rotten and diseased, healthy, broken, discolored, shriveled and pest damaged kernels are 94.9%, 96.5%, 96.3%, 97%, 97.9%, 97%, and 97.6%, respectively.
Keywords: Artificial Neural Network, quality assessment, Reconstructed Image, Image segmentation, Digital Image Processing.