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Plant part segmentation for robotic fruit harvesting using deep learning

Study level

Vacation research experience scheme

Faculty/Lead unit

Topic status

We're looking for students to study this topic.


Dr Chris Lehnert
Lecturer in Robotics and Autonomous Systems
Division / Faculty
Science and Engineering Faculty


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.

Research activities

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.