Potato cyst nematodes (PCN) (Globodera spp.) are serious pests, occupying c.48% of the potato growing area in England and Wales (AHDB, 2019) and 13.2% of production land in Scotland (Pickup et al., 2019). Chitin is one of various alternative crop protection options being investigated to reduce PCN, and is present in several industry by-products including shellfish waste composed of crustaceans, such as langoustines, crabs and lobsters, spent mushroom compost (SMC) and the frass and pupal cases associated with black soldier fly (BSF) production. This project seeks to evaluate the potential of chitinous amendments in the suppression of PCN.

MIBTP students undertake a period of training during their first year. This includes compulsory taught modules in statistics, programming, data analysis, AI and mini research projects.

MIBTP students undertake a period of training during their first year. This includes compulsory taught modules in statistics, programming, data analysis, AI and mini research projects.

MIBTP students undertake a period of training during their first year. This includes compulsory taught modules in statistics, programming, data analysis, AI and mini research projects.

AIGreenBots is a MSCA Doctoral Network that will implement inter-and multidisciplinary training, career development and research collaborations for 9 researchers (ie, PhD students), by an international and complementary partnership involving top Universities, research institutions, agriculture-related stakeholders, "living lab" and "robotic farm" infrastructures, and companies from different countries (PT, UK, FR, ES), to provide tomorrow's talent with the skills and knowledge to tackle one of the major challenges of our society and the environment - agriculture of the future. By focussing on a research-to-business/industrial exploitation paradigm, the AIGreenBots doctoral-level training brings together non-academic participants from different areas: automation, robotics, remote sensing for precision agriculture, AI and probabilistic machine learning, systems engineering, probabilistic inference, sensor fusion, safety and real-deployment of agricultural robotics. Collectively, AIGreenBots will work with the Researchers to develop new agribotics perception and sensor-fusion systems (WP4), agriculture robotic platforms (WP5), AI/ML and decision-making algorithms (WP6), safety and important legislation aspects will be learned as well (WP7). Currently Europe-wide, there are no PhD training programs that focus on agriculture-robotics that expose students to such broad concepts and expertise, yet there is a rapidly growing need for that research. After completion of the project, Researchers will be fully capable of driving interdisciplinary research at the international level. Thus, AIGreenBots' structure will serve as a European platform for outstanding doctoral training in intelligent robotics for precision agriculture farming.