Aquabyte develops machine learning and IoT camera software to enhance fish welfare and optimize aquaculture operations. By providing real-time insights and data analytics, the platform enables fish farmers to make informed decisions that improve sustainability and productivity in food production.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Aquaculture operations face challenges in maintaining fish welfare, optimizing feeding strategies, and accurately monitoring fish health and growth, often relying on manual and infrequent data collection methods. This can lead to delayed responses to disease outbreaks, inefficient resource utilization, and suboptimal production yields.
Solution
Aquabyte offers a machine learning and IoT-based camera system that provides continuous, real-time insights into fish populations within aquaculture farms. The system uses underwater cameras and AI-powered image analysis to automatically monitor fish welfare indicators, including lice counts, biomass estimation, and behavior patterns. By providing frequent and detailed data on fish health, growth, and environmental conditions, Aquabyte enables fish farmers to make data-driven decisions that improve fish welfare, optimize feeding strategies, reduce treatment costs, and increase overall productivity. The platform supports both smolt production and grow-out phases, offering tailored solutions for different stages of the aquaculture lifecycle.
Target Audience
Aquabyte targets aquaculture farms, specifically salmon farmers and smolt producers, seeking to improve fish welfare, optimize production processes, and reduce operational costs through data-driven insights.
Features
- Underwater camera system with self-cleaning lenses, designed for continuous operation in submerged environments
- AI-powered image analysis for automated lice counting, replacing manual and infrequent sampling
- Biomass estimation and growth monitoring to optimize feeding strategies and predict harvest yields
- Welfare monitoring, including detection of wounds, deformities, and behavioral anomalies
- Superior-andel (SUP) calculation to determine optimal harvest time
- Integration with farm management systems for seamless data sharing and reporting
- Predictive analytics for forecasting lice outbreaks and optimizing treatment schedules
- Remote monitoring and alerts for proactive intervention and reduced on-site labor
- Specialized camera and algorithms for monitoring smolt (small fish) production