Development Of Automatic Maize Quality Assessment System Using Image Processing Techniques
| dc.contributor.advisor | Assabie, Yaregal (PHD) | |
| dc.contributor.author | Hailemichael, Daniel | |
| dc.date.accessioned | 2018-06-18T08:31:08Z | |
| dc.date.accessioned | 2023-11-29T04:06:50Z | |
| dc.date.available | 2018-06-18T08:31:08Z | |
| dc.date.available | 2023-11-29T04:06:50Z | |
| dc.date.issued | 2015-11 | |
| dc.description.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 | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/1224 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Artificial Neural Network; Maize Quality Assessment; Reconstructed Image; Merged Image; Color Image Segmentation; Digital Image Processing; Color Structure Tensor | en_US |
| dc.title | Development Of Automatic Maize Quality Assessment System Using Image Processing Techniques | en_US |
| dc.type | Thesis | en_US |