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| dc.contributor.author | Sileabat, Dires Teferi | |
| dc.date.accessioned | 2022-11-21T07:21:51Z | |
| dc.date.available | 2022-11-21T07:21:51Z | |
| dc.date.issued | 2022-08-26 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14493 | |
| dc.description.abstract | Wheat is a crop of great importance, and as such, the market regulations governing its circulation must be followed. The quality of wheat samples is now checked manually by human experts using visual evaluation, and the contents are categorized into foreign matter, rotten and diseased, healthy, broken, discolored, shriveled, and pest damaged kernels. Visual analysis, however, calls for a large investment of time as well as skilled personnel. Furthermore, bias and inconsistent behavior inherent in human nature have an impact on it. In the absence of complete au tomation, this approach will not be adequate for large-scale inspection and grading. The objective of this research is to develop a model that can evaluate the quality of wheat sample constituents using digital image processing and Transfer Learning Techniques based on the standard for wheat set by the Ethiopian Standards Agency. In order to segment and provide the groundwork for feature extraction, a unique segmentation technique is proposed. To model the components of a wheat sample, a total of 22 features have been identified. A Vgg16-CNN network classifier using a backpropagation learning algorithm (with one hidden layer, three dense layer and one dropout layer) has been developed to classify wheat samples. It includes 22 input nodes and 7 output nodes, which correspond to the number of features and classes, respectively. The network is trained, and its effectiveness is assessed against that of other classifiers using both empirical data and evidence from the literature. A total of 2,889 kernels and foreign objects have been gathered in order to train the classifier. 70% of the data is divided at random between training and testing (30%). Overall classification accuracy for the classifier was 97.90 %. The accuracy rates for foreign, rotten and diseased, healthy, broken, discolored, shriveled, and pest-damaged kernels are 100%, 95.30%, 98.70%, 98.10%, 100%, 98.50%, and 95.20%, respectively. Keywords: Wheat quality assessment, Image Processing, Feature extraction, Segmentation, Classification | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Faculty of Electrical and Computer Engineering | en_US |
| dc.title | Wheat Quality Assessment Model Using Image Processing and Transfer Learning Techniques | en_US |
| dc.type | Thesis | en_US |
