Algal blooms, also known as harmful algal blooms (HABs), occur when toxin-producing algae grow excessively in a body of water. The origins of harmful algal blooms remain unclear, but nitrogen and phosphorus run-off from agriculture, along with the inadvertent introduction of invasive species are thought to contributing. A recent sharp increase in the poisoning of farmed fish and shellfish worldwide had led to speculation that toxic algae blooms were on the rise. Other speculations pointed to the higher density of fish farming as source.
However, in order to combat HAB at an early stage, a Canadian enterprise developed an early stage warning system, based on deep-learning image-analysis AI technology. Using the data from the AI engine, the local analysis software program and user interface generates on-site alerts for staff. It is able to for specific algal species that are of concern in a particular area as well as alerting when acceptable levels of concentration of any species of interest are exceeded. A traffic light system lets staff know if there is an issue. According to the developer, The AI Engine detects specified phytoplankton organisms with a confidence level greater than 80%.
Such developments require a differently-skilled workforce. This is where the EIT Food-funded AGAPE project aims to make a difference by providing AI-driven recommendations for upskilling and reskilling to adapt the workforce’s skills to the aquaculture industry’s changing needs.