Detection of fungal plant pathogen spores using Advanced AI and Imaging (AAII)

The project aims to develop an image-based identification system for air-borne fungal spores of important UK crop pathogens, using image analysis and artificial intelligence approaches in an automated, robotic sampling device. This method will provide real-time, continuous results of monitoring air samples, providing data on inoculum levels for multiple pathogens and allergens.

The key objective is to automate the impaction of airborne spores onto an adhesive transparent film, which will be moved past a microscope camera, focusing on three separate planes and with innovative lighting, spectroscopy, image recognition and AI used to classify spores to indicate abundance of selected pathogens. Test samples produced by Rothamsted Research in lab conditions and real farm-based air samples will be used for training and validation of the system.

The method will provide rapid identification and quantification of air-borne spores, aiming initially at three key diseases of concern in the UK. Image analysis and artificial intelligence methods will be developed to 'learn' to identify the pathogen species from known spore samples, and the algorithms developed will enable rapid identification of these pathogen spores in field samples.

The main benefits will be an accurate and fast indication of infection risk to crops for a range of diseases, which will help with disease management, especially decisions on fungicide applications. Many crops are treated more often than is necessary so as part of integrated pest(and disease) (IPM) and to reduce agricultural emissions, the project will potentially reduce fungicide applications while improving disease control.

Added benefits could be reduced spray costs for farmers and a reduced rate of development of resistance by pathogens. In addition, there will be beneficial effects on biodiversity and the environment from limiting fungicide use.

The project is led by Platform Kinetics Limited, an innovative SME specialising in deployable, automated scientific instrumentation using imaging, robotics, and artificial intelligence. The project will produce a lab-prototype system and will be tested by Rothamsted using a realistic experimental set-up. Rothamsted Research will use existing air samplers to take samples for image analysis and design of the system and will validate results by lab-testing duplicate air samples.

Private investment will be used to follow on fund this technology to commercially exploitation and integration with crop management software.