The Haricot Bean Grade Classification Using Digital Image Processing in Nigeria

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The Haricot Bean Grade Classification Using Digital Image Processing in Nigeria

Most agricultural products are the main source of food and industry input, which have a big contribution to human being day to day life activities. From most known agricultural products, haricot beans are a popular and known edible leguminous product. In Nigeria, this leguminous bean is the way to get income currency for the growth of the country. It takes 15-21 % of market exchange in the Nigerian Commodity Exchanges organization. Haricot beans products need classification based on the level of their quality. Nowadays, the process of identifying the quality of a haricot is done manually by general inspection and just by looking using the naked eye. This process takes much more extra amount of time and the quality measurement has low accuracy because it is subjective and depends on the condition of the person doing the tasks. As a result of which, controlling the quality of haricot beans is not effective and efficient. To solve this major problem the current study proposes haricot bean grade classification by using digital image processing. Digital Image processing technology is an emerging and growing technology to resolve this kind of practical and physical problem. Each level of the research was held by using experimental methods. As an experimental tool, we have used MATLAB software. For the experiment, the researcher collects sample haricot beans from the ECX laboratory and then capture them with an image quality of 3264 by 2448 pixels. The captured images have some noises that appear from the camera and environmental settings. To remove this noise media filtering technique is applied. After the binarized images were segmented by watershed segmentation techniques, the convolutional neural network is employed as a feature extractor and classifier. The researcher used the Add-16 feature extractor algorithm from the ResNet-50 package. To train the classifier we have used 300 training image set and 90 individual test set. Experimental result shows that the model achieves 90.0% grade classification of haricot bean, which is a promising result. After all, the classification algorithms have an error of 10% from the individual tests. So the researcher recommends that to minimize the rate of error happen in classification.

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