| dc.description.abstract | In the context of the Vietnamese government wanting to develop a domestic wheat
agriculture to overcome the fact that imported wheat is currently high in price and low in
availability, in this thesis, we propose an application that aids in wheat head recognition. The
application is created based on object detection and ensemble learning. Firstly, two individual
YOLOv5 models of different weights are trained and used to project bounding boxes of objects
that are possible wheat heads in an image or video. Secondly, using Weighted Box Fusion
algorithm as the post-process, the resulting boxes generated in the previous step are fused together
and the newly fused boxes’ confidence scores are recalculated based on the scores of the original
boxes that made them up. In short, this thesis aims to utilize the YOLOv5 object detection models
and the Weighted Box Fusion approach to recognize wheat head by wheat head, as well as identify
the crop distribution across an area. | en_US |
