AI and Robotics for Adaptive and Resilient Agriculture

About the project

About the Project Agriculture is becoming an increasingly challenging environment for autonomous systems. Robots must operate around changing crops, weather, terrain and farm practices, while making reliable decisions from imperfect visual and sensor information. At the same time, experienced farmers possess substantial knowledge about how and when agricultural tasks should be performed, much of which is difficult to encode explicitly. This PhD will investigate how computer vision, machine learning and robotics can enable intelligent agricultural systems to learn from human expertise, adapt to changing environments and operate safely in real-world farms . The student will join research connected to FARMAR – Farmer-in-the-loop heritage-aware AI and Robotic Mechanisation for Agricultural Resilience , a four-year Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Staff Exchanges project coordinated by Dr. Atapour-Abarghouei at Durham University. FARMAR brings together universities, technology companies and agricultural organisations to develop farmer-centred AI and robotic technologies. Potential research topics include: computer vision and multimodal perception for crops, weeds, pests and agricultural environments; learning robotic behaviours from human demonstrations and feedback; vision-language and multimodal models for translating farmer knowledge into robotic actions; continual and adaptive learning across farms, seasons and environmental conditions; uncertainty-aware and explainable AI for human-in-the-loop autonomous systems; autonomous navigation, monitoring and decision-making using ground robots and drones; coordination of multiple lightweight robotic platforms for agricultural tasks. The exact direction will be shaped around the student's interests and background. The aim is to develop new AI and robotics methods with strong publication potential , while validating them on meaningful real-world applications rather than purely benchmark datasets. A major advantage of this PhD is access to FARMAR's international research network. The consortium includes partners across the UK, Czechia, Germany, Ukraine, France, Türkiye, Finland, Vietnam, Malaysia, Thailand, Cabo Verde, Italy, Canada and Japan , offering opportunities to work with different researchers, robotic platforms, farms, crops and agricultural environments. The PhD will therefore be highly interdisciplinary and internationally connected . Depending on the research direction, the student may work alongside experts in robotics, AI, agriculture, crop science, sustainability, economics, law and human-centred technology for a period of up to 12 months. This is particularly valuable for research in agricultural robotics, where a technically successful algorithm is only useful if it can also operate safely, practically and acceptably in real farming environments. Through the FARMAR network, the student will have opportunities to develop collaborations, access different datasets and experimental settings, and gain experience working across academic, agricultural and industrial environments. As FARMAR is an MSCA Staff Exchanges project, eligible PhD researchers can undertake research secondments with project partners. Subject to MSCA eligibility and project approval, costs associated with approved secondments will be supported by the project , in line with MSCA conditions. This provides an unusual opportunity for a PhD student to spend periods working within leading international academic and industrial research environments. Supervision You will be supervised by Dr. Amir Atapour-Abarghouei ( Durham University Profile ), an Associate Professor in Computer Vision and Machine Learning at the Department of Computer Science, Durham University . His research spans computer vision, deep learning, robotic perception, and neuromorphic computing , with a focus on efficient AI for autonomous systems. Dr. Atapour-Abarghouei has published in top-tier conferences and journals , including CVPR, ICCV, ECCV, ICML, IEEE Transactions on Image Processing, and IEEE Transactions on Multimedia . His work has been widely cited, demonstrating a strong impact on the fields of AI-driven vision, robotics, and machine learning . He has been involved in multiple national and international research projects , including EU-funded initiatives and collaborations with industry leaders in autonomous robotics, AI-driven perception, and deep learning efficiency . During the PhD study, you will receive comprehensive research training , including: Regular one-to-one supervision meetings to guide your research direction, develop research independence, and refine your technical and problem-solving skills. Support in academic writing and publishing , including help with designing experiments, writing papers and targeting leading conferences and journals in computer vision, machine learning and robotics. Collaboration within a multidisciplinary research environment , working with researchers across computer vision, machine learning, robotics, autonomous systems, agriculture and related disciplines. International and interdisciplinary research secondments of up to 12 months across the FARMAR consortium. Access to advanced robotic platforms , including Unitree G1 humanoid robots, Unitree Go2 quadruped robots, mobile ground robots, autonomous vehicles and aerial drones , providing opportunities to develop and test AI methods on real embodied systems. Access to a wide range of sensing technologies , including RGB and depth cameras, LiDAR, RADAR, inertial and localisation sensors, environmental sensors, EEG equipment and other multimodal sensing systems , supporting research in perception, sensor fusion and intelligent autonomous systems. Training in interdisciplinary research and communication , including working with collaborators from areas such as agriculture, biological sciences, sustainability, economics and human-centred technology, helping the student understand how advanced AI and robotics can be translated into practical solutions. Dr. Atapour-Abarghouei has supervised numerous undergraduate, MSc, and PhD students , many of whom have gone on to pursue successful careers in research, academia, and industry . His approach to supervision emphasises independent thinking, problem-driven research, and a supportive learning environment to help students develop into well-rounded researchers with expertise in cutting-edge AI and robotics technologies . Durham University Durham University is a world top-100 university and is ranked the 6th in the UK. As a member university of the elite Russell Group, Durham University focuses on research excellence delivered by world-leading academics. It is the third oldest university in England, following Oxford and Cambridge, with the campus's Cathedral and Castle being a UNESCO world heritage. It is located at Durham in North East England – one of the safest cities in the UK with an affordable living cost. Entry Requirements A relevant undergraduate or master's degree with good scores. Knowledge of modern programming languages. Meet Durham's English requirements ( https://www.dur.ac.uk/study/international/entry-requirements/english-language-requirements/ ). How to Apply Please send an email with your resume, transcripts, and any supporting documents to Dr Amir Atapour-Abarghouei at amir.atapour-abarghouei@durham.ac.uk for an initial discussion.

Research areas