Project Summary

The SETU team will be tasked with assessing the feasibility of a variety of vision / digital systems to identify parameters of interest (PoE’s) or biological markers across a variety of crops (in-field) for feature extraction potential. These key parameters of interest (PoE’s) will form a digital foundation on which to improve processing and increase national food security.

Why This Scholarship Stands Out

This PhD is unique because it integrates digital agriculture, computer vision, and food processing. You will work at the intersection of field-based sensing (drones, in-field analysis), AI-driven image analysis, and shelf-life mapping. The project focuses on Irish-grown crops (fava bean, pea, lupin, oats) for plant-based ingredient extraction – a growing sector in Europe. You will collaborate with researchers at Teagasc, UCC, UCD, and Irish food and agricultural companies, providing a strong national research network. For a student interested in digital agriculture, computer vision, and food systems, this is an opportunity to develop skills across image analysis, machine learning, and precision agriculture.

Main Tasks

• Apply digital imaging techniques on data captured from drones and in-field analysis to identify points of interest, determine digital vegetation index, digital plant health and yield, and estimate composition
• Complete commercial profiling assessing intrinsic and extrinsic factors impacting product/ingredient readiness
• Digitally map product shelf-life, through a series of product/ingredient or supply route-specific, temporal, and dynamic profile conditions

Candidate Profile and Eligibility

Desirable:

• Strong academic background in digital imaging
• Strong academic background in precision agriculture
• Postgraduate diploma/MSc in Artificial Intelligence, precision agriculture, remote sensing
• Experience with image analysis, computer vision, machine learning, or artificial intelligence
• Knowledge of crop science and agricultural or food systems
• Knowledge of food analysis
• Interest in image analysis, digital agriculture
• Familiarity with statistical analysis and interpretation of experimental data
• Ability to manipulate and analyse datasets using programming or scripting
• Experience in statistical or programming-based environments

What They Offer

My Application Tips

1. Highlight your computer vision or image analysis experience – This is core to the project
2. Emphasise precision agriculture or remote sensing background – Drone-based imaging and in-field analysis
3. Show programming skills – Python, machine learning, or statistical programming
4. Demonstrate interest in plant-based ingredients and food systems – The project focuses on fava bean, pea, lupin, and oats
5. Mention any experience with AI or machine learning – AI-driven image analysis is a key component

Who Should Apply

This PhD is perfect for a student with a background in computer science, engineering, agriculture, or environmental sciences who wants to apply digital technologies to agriculture and food processing. If you are interested in how drones, computer vision, and AI can improve crop monitoring, ingredient extraction, and shelf-life prediction, this project offers training across digital agriculture, image analysis, and food science.

Useful Links

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