IoT as an enabler of AI: Revolutionizing fish production

The Internet of Things (IoT) is a hardware/software ecosystem that includes the ‘things’ side, typically composed of network-enabled sensors (light, temperature, distance), which connect and exchange data with actors (motors, servos, heaters, pumps). This technology enables AI by providing a constant stream of real-time or historical data while assuring its quality. This particular process ensures that AI applications achieve optimal results when solving data-driven problems. Applying AI in remote areas where continuous connection to the internet is not guaranteed requires that AI solutions have to be scalable and fail-safe to avoid data loss and provide consistent performance.

The Internet of Fish

The marine environment is suffering from unsustainable fishing practices. Traditional aquaculture is not an environmentally friendly alternative, as most methods do not completely separate themselves from the natural environment, thus continuing to impact the surrounding marine ecosystem.
Farms can be installed on any piece of land to produce fresh seafood close to the consumer
BPE has taken a different approach by replicating natural aquatic ecosystems. It achieves this by building bioreactors that convert sunlight into algae, algae into plankton, and then plankton into fish. Bioreactors are equipped with sensors that generate data representing the state of health of the ecosystem. Actors include light switches, pumps, feeders and heaters that are enabled at the appropriate time so the ecosystem remains healthy. By taking a data-driven approach BPE, supported by Cloudflight implementing the required cloud based components, have created sustainable seafood sources in urban and desert environments.

IoT Enables AI

IoT enables data collection to train AI models that balance the various input parameters. These are transmitted back to the actors to maintain an optimal state of the ecosystem. The IoT system consists of:
  • A hardware-software ecosystem;
  • A software platform that manages the ‘things’ and the data they generate;
  • Feedback loops that translate the analysed data into new input for the ’things’ (e.g., a higher target temperature based on school movement observations that indicate that the fish are affected by cold);
  • A repository of data-based user applications such as dashboards for visualization or machine learning models for predictive maintenance.

Converting Data into Fish

Fish-farming is costly and labour-intensive. The BPE artificial ecosystem is completely automated and uses only a few resources, “manufacturing” fish independently of human intervention.
A LARA stack consisting of three levels: the photobioreactor, the zooplankton unit and the fish unit
The IoT sensors are used to constantly measure many parameters including temperature, light and oxygen content of the water. Video streams from several cameras detect and monitor the fish and their movements. From these videos, BPE is able to extract biological data. This is done by applying object detection to first recognize all fish in frame and later deduce their vitality by tracking and reconstructing the swarm movement across multiple frames and assigning a “vitality” value to each individual fish. The information derived from the sensors is used to regulate the state of the system to optimize the well-being of the fish and their growth. In addition, all values are recorded and analysed in order to optimize the bioreactor. Manual control is, however, always possible via a web interface to enable intervention in the event of any problems.

The Role of Domain Experts

Data scientists and AI experts alone can rarely be used to improve processes. Often it is the combination of domain knowledge (in BPE’s case, aquaculture experts), data scientists and enablers such as IoT that work together to tackle problems that previously seemed unsolvable. Knowing about how the data is collected and initially processed impacts the work of data scientists. For example, a machine that is switched off over the weekend might produce “anomalous” data on Monday mornings just because it needs to heat up first. Knowledge such as this can easily be gained by involving domain experts. If the project team cannot rely on the presence of domain experts then the amount of data that needs to be collected to achieve the same confidence is usually higher. By combining expertise from domain experts, such as BPE in fish farming, and technical expertise from Cloudflight, novel solutions can be found to critical human problems. In this case we can see an innovative and environmentally friendly solution to fish farming.