Job: Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit

Title Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit
Categories funded, PhD
Salary/Bursary £14,777
Location University of Lincoln
Job Information

Background

Novel digital technologies including vision systems, robotics and autonomous systems are seen as potential game changers for the horticulture sector. Visions systems can be used to assess and sense the crop to enable better decision support; robotics and autonomous systems offer new means to drive productivity. These issues apply to all soft and top fruits, but also more widely across the whole fresh produce sector. However, all picking and vision systems are dependent on the development of complex algorithms developed to identify, measure and locate fruit in real time. The development of these systems is not trivial, especially in outdoor environments where the background light level and quality can change within an instant.

Approach

The main objective of this project is to deploy novel machine learning technologies to detect, locate and measure (size and colour) fruit in real time. This work fundamentally underpins the development of all crop-picking robots. The student will use and develop advanced machine learning algorithms to measure, identify and detect fruit in real time and in 3D. The algorithms will be trainable (so that a range of fruit types can be identified) and provide world coordinates of the fruit. Researchers from the Lincoln University (Kusumam et. al, 2017) have developed similar systems for broccoli. This earlier work showed that 3D cameras could be deployed in field environments however the algorithms were highly complex with relatively slow processing speed. The new challenge for this PhD project will be to minimise processing requirements to identify fruit whilst maximising processing speed and recognition fidelity. This project will initially focus on strawberry and be anticipated to include apple.

Project

The project is funded by the Collaborative Training Partnership for Fruit Crop Research funded by BBSRC and Industry. The supervisors are Dr Bo Li (NIAB EMR) and Dr Grzegorz Cielniak (University of Lincoln).

Candidate

We are looking to recruit a PhD student with relevant experience and/or a strong interest in research areas including (but not limited to):

  • image acquisition, sensing and image processing;
  • computer vision, computational geometry, 3D perception;
  • real-time processing, parallel computing;
  • machine learning, deep neural networks.

You should have a good Bachelors or Masters in Computer Science, Electronic Engineering, Mathematics or Physics. You must have excellent mathematical and coding skills (C++/Python/Matlab), and be available to start work on the project as soon as possible. The PhD studentship offers the opportunity to engage in international collaboration within an ambitious team and to benefit from excellent support to produce and disseminate original research contributions in the leading international conferences and journals. You must be able to work collaboratively as part of a team, including excellent written and spoken communication skills.

How to Apply

The position is fully-funded (UK/EU candidates only, unfortunatelly) and covers fees, UKRI studentship level stipend and additional cover for attending conferences, travel and equipment. Anyone interested should send the application (CV, cover letter, personal statement and two names for reference) in one PDF by clicking the “Apply now” link below. Questions can also be asked in an email to gcielniak@lincoln.ac.uk. Applications will be assessed as they arrive but not later than 1st of July, 2018. If appropriate, we will contact applicants to discuss things further.

 

Apply Now