To embed power engineering knowledge to allow development of the fish stunning system by establishing processes, codifying and documenting results, thus allowing simulation of new concepts and exploration of design infrastructure to allow prodution of a MK2 new concept stunning system.

A project to develop AI cameras for salmon harvest stations to improve the accuracy of fish count, weight capture and fish welfare at slaughter. Full validation of system accuracy assessed by research partner Silsoe livestock Systems.

no public description

Fish farms waste feed (60% of production costs, 'Mowi Handbook 2022) by over feeding dominant fish, and underfeeding smaller fish (which leads to stress, higher incidence of disease and parasites, such as sea lice). Farmers attempt to rectify this through manual random sampling (30-40 fish per pen, on 12 cage sites) to provide indications of low/average/high fish weights. There are around 400 live sites in Scotland each year with typically 12 cages per site containing 15-17.5kg of fish per cubic tonnes of seawater (1.5/98.5 water to fish ratio). Stocking density is heavily regulated so accurate biomass data is critical for compliance. Typically a farm will grade out smaller fish into a new pen to bring all fish up to the required harvest weight. However, inaccurate data (due to sampling) leads to many fish being harvested above or below the required weight. Supermarkets do not compensate for under or over production. Wasted 'consumed' feed (at £1300 per tonne) costs farmers a significant proportion of their income (every kilo over the required weight costs £4.43, when the average cost of a fish is bought for £40 (Kontali 2019)). Tools such as Vaki's Biomass Daily frame, Akva's Vikas, or several new AI cameras (Aquabyte, Optoscale) have attempted to automate the weight estimation process; but limited visibility throughout the pen leads to misleading distribution data. While farmers have better tools to track average growth in the pen ensuring compliance, they have little help when accurately assessing when to grade fish to prevent feed waste. Ace's innovation utilises multiple stereo cameras embedded in a 360 arrangement to provide the first 'depth' point of cloud mapping and AI weight estimation, capable of tracking fish in all parts of the cage providing high fidelity distribution accuracy, and a clear portal for informing fish husbandry and saving money.

To develop, test and deploy innovative Artificial Intelligence based approaches to improve accuracy of Individual fish identification and sea lice detection

A novel method of electically stunning farmed fish