Automatic Soybean Quality Grading Using Image Processing and Supervised Learning Algorithms
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Date
2021-10-12
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Addis Ababa University
Abstract
Soybean is one of the most important oilseed crops of the world which requires 25 to 30°C temperature for growth and proper modulation. Due to its high protein content and nutritional quality soybean usually used in food preparation, animal feed and industry sector. It is an input for food products like soy milk, for human consumption and as in put for industry for production like paper, plastic and cosmetics. The trading of soybean in Ethiopia is done through Ethiopian Commodity exchange internally as well as for export trading. Determining the quality grade of soybean is crucial in the trading process. It improves the production of quality soybeans and it helps to become competent in the market. This process is done manually in Ethiopian Commodity Exchange which is subjected to different problem: less efficient, inconsistent and vulnerable to subjectivity.
As a solution in this thesis we propose an automated quality grading of soybean using image processing techniques and supervised learning algorithms, which is the aim of this thesis. Image acquisition, image pre-processing, image segmentation, predict soybean type and determining the grading are the major steps that are followed. For image preprocessing, methods like median filter to remove noise, modified unsharp masking sharpening technique is used to enhance the quality of acquired soybean image. In image segmentation a modified Otsu‟s threshold segmentation method is used to apply to a color image. Nineteen typical characteristic parameters of samples are extracted as the characteristic soybean, which are 7 morphological, 6 colors and 6 texture features. Three different supervised learning algorithm classifiers are applied and compared: support vector machine algorithm, artificial neural network and convolutional neural network.
Experimental results show one dimensional convolutional neural network outperforms the others with accuracy rates of 93.71% on the test datasets collected from Ethiopian Commodity Exchange. We concluded that the CNN is superior to other supervised learning algorithm, and using aggregated features is better than using single type of features.
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Keywords
Soybean Kernel, Quality Grading, Image Processing, Support Vector Machine, Artificial Neural Network, Convolutional Neural Network and Supervised Leaning Algorithms