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Using Artificial Intelligence to Promote Food Security

   Department of Earth and Environmental Sciences

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  Prof Giles Johnson, Prof Hujun Yin, Prof Bruce Grieve  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Maintaining food security, with growing populations and changing climates, is one of the most important challenges facing humanity. In agriculture, this requires us to increase crop yields whilst reducing chemical inputs. Precision agriculture is one of the most promising and exciting approaches to address this, allowing targeted application of chemicals to minimise environmental impacts.

In this PhD project you will apply cutting edge AI approaches to understand plant nutrition and stress, using a novel sensor technology developed in Manchester – Sinusoidally Modulated Fluorescence Imaging (SMFI). SMFI generates big datasets (T-bytes) for every plant within a study comprised of a series of complex (phasors) harmonic parameters for every pixel at every fluorescence excitation period within an imaged crop sample. The student will test the hypothesis that SMFI can be data-driven to identify early impacts of environmental stress, allowing it to be applied to problems in agriculture. The initial exemplar system will be based on winter wheat species and abiotic stresses associated with nutrient deficiencies.

The first aim is to deliver Artificial Intelligence (AI) or Machine Learning (ML) approaches that quantitatively relate SMFI imaging to early-stage nutrient deficiency in plants. A second aim is to link that information to the underlying photosynthesis, and primary and secondary metabolism within plants.

The supervisory team consists of Prof Giles Johnson, a plant physiologist, Prof Hujun Yin, an expert in machine learning, deep learning and image processing, and Prof Bruce Grieve, an expert in sensor systems for food and agriculture.

This project has both practical and theoretical aspects. The student will design and execute a series of controlled plant stress trials, using prototype SMFI sensor and equipment developed in on-going research programmes. The student will work alongside biologists, gaining practical skills in plant physiology. Data collection will be performed iteratively for modelling. The student will research and deliver new model-based AI methodologies that can accommodate and deconvolute interacting factors as well as to manipulate the modulation of actinic (growth) light in a closed-loop manner, to maximise the information content of the data-arising. AI/ML methods are becoming an appealing alternative to solving many physical modelling or data driven inverse problems. Deep neural networks (DNNs) constructure a complex relationship using a large number of simple functions arranged in multiple scales or levels, further coupled with supressing or subsampling to achieve flexibility and stability. Generative adversarial networks (GANs) are an adaptive framework for learning universal functionals. Injecting biological models into deep learning will make deep learning more focused and stable. This project will explore the best ways to integrate biological mechanisms in plant growth such as photosynthesis, minerals, nutrients and water, into DNNs for modelling their complex growth patterns and functions. Temporal models or mechanisms will also be integrated to the modelling networks via constraints or adding temporal features. Controlled trials will be conducted for data collection, model development and verification. The predictive and diagnostic power of modelling based on laboratory studies will be tested under greenhouse and then field conditions.

This research will bridge the gaps of information science, applied biology and sensor systems research. Through utilising knowledge of techniques and approaches from across all three disciplines it is anticipated that the student will develop a ‘common language’ allowing them to interact with and influence PhD and Early Career Researchers across the UoM community in those disciplines.

The ideal candidate for this position will have:

  • A good undergraduate degree (at least 2.1, or equivalent, completed or graduating before Sept 2022) in Applied Mathematics, Mathematical Modelling or Biological Modelling, Computer Science or Electronic Engineering. Experience at Masters level would be an advantage.
  • Excellent communication and interpersonal skills
  • Excellent time management and organisational skills
  • Ability to work independently and as part of a team
  • Willingness to learn and develop

For project enquiries contact Profs. Giles Johnson and Hujun Yin

To make an application please visit -

Please search and select Environmental Science (academic programme) and  PhD Environmental Science(academic plan)

Funding Notes

This is a funded studentship for UK students starting in September 2022

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