Publication: Ginger seed growth recognition using mask region based convolutional neural network (mask r-cnn)
| datacite.subject.fos | oecd::Engineering and technology::Mechanical engineering | |
| dc.contributor.author | Tong Yin Syuen | |
| dc.date.accessioned | 2025-05-19T03:37:39Z | |
| dc.date.available | 2025-05-19T03:37:39Z | |
| dc.date.issued | 2023-01-01 | |
| dc.description.abstract | As a plant that poses unique culinary and medical uses, ginger has emerged as a valuable commodity in Asia. Among the critical processes in the production of ginger is ginger seed preparation. It is particularly important to monitor the growth and quality of ginger seeds before they are being sown in growing media to ensure germination. However, to date, the ginger seed monitoring process remains manual and is reliant on human experts, despite the growing demand for more effective and accurate monitoring. In this work, a total 1,746 images consisting 2,230 sprout instances were collected from 282 ginger seed samples. In order to realize the automatic monitoring of ginger seeds, deep learning architectures were employed to detect the ginger seed sprouts in three stages from the digital images. This work assessed and compared the instance segmentation task using end-to-end Mask R-CNN models built by different strategies. Then, a two-stage hybrid detector-classifier model was also proposed to benefit from model task specialization concept. Specifically, an end-to-end binaryclass Mask R-CNN and multi-class classifier were combined to be compared to an end-to-end multi-class Mask R-CNN. The experimental results indicate that the use of the hybrid detector-classifier model developed in this work achieved mAP0.50 of 84.27% at inference time of 0.383 second per image in the detection of 402 images consisting of 514 sprout instances. Besides, substantial confusion between object classes in the model was also observed to be in line with the human expert’s perception in data annotation. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/21700 | |
| dc.language.iso | en | |
| dc.title | Ginger seed growth recognition using mask region based convolutional neural network (mask r-cnn) | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |