Digitally recording and evaluating shrimps’ behaviour to significantly increase animal welfare, health and operational safety in land-based farming. MonitorShrimp’s innovation is a digital technology using AI to analyse real-time data from purpose-built cameras, hydrophones and standard water quality sensors in land- based farming systems for shrimp. The system provides farmers with information about their system’s biomass and condition at any given time, allowing them for the first time to operate their system and make operational decisions based on actual biomass and condition data. MonitorShrimp improves animal health and welfare, increases feed and protein efficiency by 25-30% and reduces wastewater nutrient load and solid waste by at least 20%. Production efficiency also increases as higher yields are achieved with the same or lower input of resources. Plant builders and operators can also use MonitorShrimp’s digital data collection and AI evaluation to estimate plant expansions and compare plant types and production locations. As a result, MonitorShrimp will lead to a paradigm shift in
land-based shrimp farming. Less

Developing innovative species-specific bioindicators to evaluate fish’s farming, health, and product quality in different aquaculture environments.

Biochip-based molecular indicators will be used to assess aquatic organisms’ welfare in real-time at various production stages, certify husbandry conditions of aquaculture facilities and optimise fish production processes. To do so, we’ll carry out comprehensive monitoring of the husbandry conditions via physicochemical and image sensors, while the data analysis will be conducted using AI algorithms.

The project will focus on recording and reducing negative influences in aquafarming, especially at juvenile stages of development, improving the mechanical processing of fish, and analysing the impact of microbiological parameters on the health of aquaculture organisms.

Integrating an automated monitoring system into the control concept of RAS and RACE systems, first for aquaculture and later for pond and offshore systems.

This monitoring system enables the analysis of animal welfare using neural networks for data analysis, cloud-based software and specially adapted hardware. Both RAS and RACE systems require an early and predictive assessment of their condition and animal welfare, to allow problems to be detected at an early stage and countermeasures to be taken before threats to aquaculture organisms become imminent.

We use intelligent algorithms to link and evaluate physical, chemical and image sensor data, which can then be sent to the corresponding operators. An essential part of the project is the adaptation of the algorithms to the systems.

Develop a plug-and-play camera system that can be used in above and underwater environments and installed on-site by non-professionals.

We are creating a compact stereo camera system that can be shipped in the most user-friendly way possible. The system will also be equipped with integrated remote controllable lights and sensors, enabling it to communicate with the operator via basic Human-Machine Interaction (HMI).

In addition, we are also developing an assembly system that can be used in different environments such as chicken farms, fish farms and RAS systems with varying tank configurations.

Developing a computer vision system for animal breeding.

In the last decades, production on farms has expanded considerably and the specialisation of individual farm branches has led to animal population increases. As a result, it is no longer possible for farmers to conduct individual animal monitoring, especially broiler fattening. Instead, farmers can now turn to automated monitoring systems, which have shown enormous potential to meet these increasing expectations, especially for German livestock farming.

We are developing an indicator-based early-warning system that can record animal behaviour even in large herds and trigger an alarm in the event of conspicuous behaviour. The constant observation of animals will help farmers recognise different behaviour patterns and initiate early-stage countermeasures. One component of the project is to examine the potential of neural networks and artificial intelligence in livestock barns and explore the extent to which these technologies can support or even replace a farmer’s eyes and ears

Developing an e-aquaculture monitoring system using the Internet of Things

We have created a novel, non-invasive monitoring concept and system for selected aquaculture organisms (AKO) using physicochemical and image sensor technology via cloud-based software.

This system provides an early assessment of stress levels, health and animal welfare of aquatic organisms at different stages of production from hatching to harvest weight. In addition, the software can certify farming conditions and aquaculture facilities, enabling the real-time optimisation of fish production processes.