Funded PhD Studentship: Annual DVC Award- Aerial Very- and Ultra-Low Volume Variable Rate Spraying for Autonomous Arable Systems

Agriculture & Environment

Project Title:  Aerial Very- and Ultra-Low Volume Variable Rate Spraying for Autonomous Arable Systems

Primary supervisor: Dr Nwabueze Emekwuru

Co supervisors: Professor Fernando Auat Chein, Dr Simon Woods

Expected Start date and location

October 2026 onwards, based at Harper Adams University, Edgmond, Shropshire, UK.

Funding

The studentship covers the current Home Student (UK, Isle of Man & Channel Isles) tuition fees plus a yearly stipend. For 2026/7 this equates to £21,805 per year, with potential increases each academic year.

International applicants would need to be able to fund the difference between home and overseas fees with a proportion being paid in full before Visa documentation can be issued.

Applicants

PhD applicants must hold a minimum of an upper second class (2:1) honours degree, or equivalent in a relevant discipline or a 2.2 alongside a relevant Master's degree with Merit, or potential for research based on alternative qualifications/experience judged acceptable by the university.

Project educational requirement: A first-class or strong upper second-class undergraduate degree (or equivalent) in Agricultural Engineering, Biosystems Engineering, Mechanical Engineering, Environmental Engineering, or a related discipline

Essential: 

• Strong analytical and quantitative skills. 

• Excellent written and spoken English communication skills. 

Desirable: 

• Knowledge of fluid dynamics, especially experimental methods in atomisation and sprays. 

• Programming experience (e.g. Python, MATLAB or similar). 

• Experience with data analysis, machine learning or geospatial datasets. 

• Experience with UAVs, precision agriculture or remote sensing. 

• Experience with writing for publications.

Project 

Autonomous arable farming has advanced significantly through the Hands-Free Hectare and Hands-Free Farm initiatives, demonstrating that crops can be established, managed and harvested without in-field human operators. These systems integrate GNSS-guided machinery, remote sensing and data-driven decision-making to deliver commercially viable yields. However, crop protection remains largely

dependent on conventional ground-based spraying, limiting operational flexibility and environmental optimisation.

Unmanned aerial vehicles (UAVs) are increasingly used globally for pesticide applications, enabling highly targeted interventions, reduced operator exposure, and significantly lower pesticide use. Despite this, there is limited understanding of UAV spray behaviour under temperate UK very-low volume and ultra-low volume formulations operating conditions and no fully integrated framework linking UAV-based sensing, decision-making and variable-rate aerial application within autonomous farming systems.

This research studentship will address the challenge of integrated UAV-based variable-rate spraying system for autonomous arable production. The project combines hyperspectral sensing, machine learning, and spray physics solutions to the emerging area of precise, data-driven crop protection.

The research will have access to in-house and external partner spray labs, the Hands-Free Farm, a soil hall and the UK government agency governing UAV sprays.

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