For a robot to successfully harvest fruit it needs to understand what parts of the plant should be avoided, such as the main stem or branches of a plant.
The aim of this project is to create a deep learning system for detecting and segmenting key parts of plants for use in robotic fruit harvesting. This work will look at developing a semantic segmentation deep learning system that can identify the key parts of the plant for use on a QUT-developed robotic fruit harvester.
As part of the research project, you will be involved in:
- training deep convolution neural networks for semantic segmentation
- testing deep neural networks for performance on real world environments.
On completion of the project, we expect to have created:
- a semantic segmentation method for classifying and segmenting different parts of plants
- a performance analysis of the semantic segmentation method.
Skills and experience
To be considered for the research project, you should have skills and/or experience in:
- Python programming
- understanding of deep neural networks
- machine learning techniques such as supervised learning
- computer vision techniques, such as image-based object classification and image-based object segmentation.
Contact the supervisor for more information.