dc.contributor.author | Shin, Jaemyung | |
dc.date.accessioned | 2020-04-16T16:39:50Z | |
dc.date.available | 2020-04-16T16:39:50Z | |
dc.date.issued | 2020-04-16T16:39:50Z | |
dc.identifier.uri | http://hdl.handle.net/10222/78584 | |
dc.description.abstract | This research proposed the algorithm, that can detect powdery mildew and give the highest classification accuracy (CA). Three image processing and two machine learning algorithms (artificial neural network; ANN and support vector machine) were used to find the optimal combination for different image resolutions. Also, data augmentation by using a rotation technique was carried out to simulate the real-world field-situation. The results after data augmentation tended to be underfitting due to the added directional parameter. The results showed the highest CA of 94.34% for the combination of speeded-up robust features and ANN at 908×908 image size. To get better performance, six convolutional neural network algorithms were compared after data augmentation. ResNet-50 as the highest CA, AlexNet as the shortest computation time, and SqueezeNet-MOD2 as the smallest memory would be recommended in the conclusion. | en_US |
dc.language.iso | en | en_US |
dc.subject | powdery mildew | en_US |
dc.subject | machine learning | en_US |
dc.subject | deep learning | en_US |
dc.subject | disease detection | en_US |
dc.title | SUPERVISED MACHINE/DEEP LEARNING TECHNIQUES – A CASE STUDY OF POWDERY MILDEW DETECTION ON THE STRAWBERRY LEAF | en_US |
dc.date.defence | 2020-03-16 | |
dc.contributor.department | Faculty of Agriculture | en_US |
dc.contributor.degree | Master of Science | en_US |
dc.contributor.external-examiner | n/a | en_US |
dc.contributor.graduate-coordinator | Gordon W. Price | en_US |
dc.contributor.thesis-reader | Tri Nguyen-Quang | en_US |
dc.contributor.thesis-reader | Gordon W. Price | en_US |
dc.contributor.thesis-reader | Ahmad Al- Mallahi | en_US |
dc.contributor.thesis-supervisor | Young Ki Chang | en_US |
dc.contributor.ethics-approval | Received | en_US |
dc.contributor.manuscripts | Yes | en_US |
dc.contributor.copyright-release | Yes | en_US |