Methane is an extremely potent yet short-lived greenhouse gas and is thus recognized as a promising target for rapid climate change mitigation. About 35% of anthropogenic methane emissions are associated with livestock production, and most of these emissions are the outcome of enteric fermentation in ruminant animals. The red seaweed Asparagopsis is currently considered the most efficient feed additive to suppress methane emissions from enteric fermentation but is not currently available on commercial scale. The ongoing effort to successfully commercialize Asparagopsis requires the development of pest control frameworks which rely on the quantitative assessment of biological contamination in cultures. Here we present a low-cost readily available approach for quantifying biofouling in Asparagopsis taxiformis cultures based on microscopic examination and automated image analysis. The proposed methodology is demonstrated to estimate contamination associated with Asparagopsis biomass with error rates lower than 2% over a wide range of contamination levels and contaminating organisms, while significantly cutting down image processing time and allowing for frequent contamination quantification.

Methane is an extremely potent yet short-lived greenhouse gas and is thus recognized as a promising target for rapid climate change mitigation. About 35% of anthropogenic methane emissions are associated with livestock production, and most of these emissions are the outcome of enteric fermentation in ruminant animals. The red seaweed Asparagopsis is currently considered the most efficient feed additive to suppress methane emissions from enteric fermentation but is not currently available on commercial scale. The ongoing effort to successfully commercialize Asparagopsis requires the development of pest control frameworks which rely on the quantitative assessment of biological contamination in cultures. Here we present a low-cost readily available approach for quantifying biofouling in Asparagopsis taxiformis cultures based on microscopic examination and automated image analysis. The proposed methodology is demonstrated to estimate contamination associated with Asparagopsis biomass with error rates lower than 2% over a wide range of contamination levels and contaminating organisms, while significantly cutting down image processing time and allowing for frequent contamination quantification.