Computer-Simulated Virtual Image Datasets to Train Machine Learning Models for Non-Invasive Fish Detection in Recirculating Aquaculture.

Artificial Intelligence (AI) and Machine Learning (ML) can assist producers to better manage recirculating aquaculture systems (RASs). ML is a data-intensive process, and model performance primarily depends on the quality of training data. Relatively higher fish density and water turbidity in intensive RAS culture produce major challenges in acquiring high-quality underwater image data.

Anaerobic digestion challenges and resource recovery opportunities from land-based aquaculture waste and seafood processing byproducts: A review.

The unprecedented demand for seafood has resulted in land-based recirculating aquaculture systems (RAS), a highly intensive but sustainable fish farming method. However, intensification also results in concentrated waste streams of fecal matter and uneaten feed. Harvesting and processing vast quantities of fish also leads to the production of byproducts, further creating disposal challenges for fish farms.

Denitrifying woodchip bioreactor and phosphorus filter pairing to minimize pollution swapping.

Pairing denitrifying woodchip bioreactors and phosphorus-sorbing filters provides a unique, engineered approach for dual nutrient removal from waters impaired with both nitrogen (N) and phosphorus (P). This column study aimed to test placement of two P-filter media (acid mine drainage treatment residuals and steel slag) relative to a denitrifying system to maximize N and P removal and minimize pollution swapping under varying flow conditions (i.e.

Denitrifying bioreactor clogging potential during wastewater treatment.

Chemoheterotrophic denitrification technologies using woodchips as a solid carbon source (i.e., woodchip bioreactors) have been widely trialed for treatment of diffuse-source agricultural nitrogen pollution.

Factors associated with the incidence of bacterial gill disease in salmonid lots reared in Ontario, Canada government hatcheries.

Bacterial gill disease (BGD) (causative agent: Flavobacterium branchiophilum) has been a persistent problem in early-rearing salmonids in the Ontario Ministry of Natural Resources (OMNR) fish hatchery system. Retrospective epidemiological investigations of BGD diagnoses and treatments in OMNR fish hatcheries during the period 1991-2001 were conducted using University of Guelph Fish Health Laboratory and OMNR central office data. All investigations were conducted at the lot-level, which is the major within-hatchery-level of population aggregation.