South African scientists are using artificial intelligence (AI) to detect and classify pollen spores in real time, with two AI counters already set up in Pretoria and Kimberley, to help improve the forecasting of bioaerosols that trigger respiratory illnesses such as allergic rhinitis (hay fever) and asthma.

The monitoring of airborne pollen provides an important source of information for the rapidly rising number of hay fever and asthma sufferers, and more AI counters will come on line in Cape Town, Durban, Bloemfontein and Johannesburg in due course.

“In South Africa, nearly 30% of people suffer with allergic rhinitis and a recent study from KwaZulu-Natal found that 13.7% of adolescents suffer from asthma, of whom 9% suffer from severe asthma,” says head of the Allergology and Clinical Immunology division at Groote Schuur Hospital and the University of Cape Town Lung Institute Professor Jonny Peter.

Peter, who also leads the South African Pollen Network, explains that, traditionally, scientists manually counted airborne pollen and spore types using volumetric air samplers, collecting the particles trapped on adhesive tape, which are then analysed under a microscope by an expert.

The process is cumbersome and costly but with the latest developments in image recognition methods and machine learning, automating this process is now feasible.

Peter says a system capable of identifying and categorising pollen more accurately – and at much faster rates – can now be built by combining cutting-edge technologies, like AI and imaging flow cytometry, which measures the size, count, shape and structure of a cell.

“Pollen grains from different plant species are unique and identifiable based on their individual features. Analysing which pollen grains are captured in samples from sediment cores in lakes can help scientists get a better grasp on which plants were thriving at any given point in history, potentially dating back thousands to millions of years,” he says.

Further, AI can provide a more comprehensive picture of pollen in the present, as well as help to model historical environmental change.

“Its numerous applications are transforming the way scientists conduct research and is enabling new discoveries across fields – accelerating scientific productivity.”

The system works in a similar manner to the volumetric spore trap that sucks the pollen on to the cellulose membrane, with the addition of images. AI is trained using large volumes of images to recognise the individual grains on the sticky tape.

Currently, there is a need for a period of validation, with a conventional volumetric trap set next to the automatic AI counter to verify the accuracy of the analysis.

“Once the AI has been sufficiently trained to recognise grains that are indigenous to South Africa, it will be able to classify species very quickly,” Peter comments, noting that main allergens, such as grass, plane- and cypress trees, as well as fungal species, such as Alternaria, Aspergillus and Cladosporium, should be easy for the AI to identify as these are commonly found worldwide.

“Pollen counts are often difficult to predict during spring when counts are the highest, and the weather is changing daily. Having access to reliable, real-time pollen readings will significantly improve forecasting and, in turn, help hay fever sufferers to mitigate bothersome symptoms that include a runny or itchy nose, sneezing, itchy and watery eyes, as well as nasal congestion.”

According to Peter, seasonal allergies affect an estimated 18-million South Africans.

“This year’s pollen season depends largely on the weather, but hay fever sufferers can expect higher than normal grass counts. We are already seeing high tree counts in Cape Town with exceptionally high tree counts in Bloemfontein.”

He adds that the El Niño weather phenomenon, expected to make its way to South Africa later this spring, could also influence the timing and severity of this year’s allergy season.

While it is typically associated with hot and dry conditions in South Africa, certain regions could receive increased precipitation, as well as higher-than-average temperatures, leading to enhanced pollen production.