Development of a low-power underwater NFC-enabled sensor device for seaweed monitoring

Aquaculture farming faces challenges to increase production while maintaining welfare of livestock, efficiently use of resources, and being environmentally sustainable. To help overcome these challenges, remote and real-time monitoring of the environmental and biological conditions of the aquaculture site is highly important. Multiple remote monitoring solutions for investigating the growth of seaweed are available, but no integrated solution that monitors different biotic and abiotic factors exists. A new integrated multi-sensing system would reduce the cost and time required to deploy the system and provide useful information on the dynamic forces affecting the plants and the associated biomass of the harvest. In this work, we present the development of a novel miniature low-power NFC-enabled data acquisition system to monitor seaweed growth parameters in an aquaculture context. It logs temperature, light intensity, depth, and motion, and these data can be transmitted or downloaded to enable informed decision making for the seaweed farmers. The device is fully customisable and designed to be attached to seaweed or associated mooring lines. The developed system was characterised in laboratory settings to validate and calibrate the embedded sensors. It performs comparably to commercial environmental sensors, enabling the use of the device to be deployed in commercial and research settings

Wave height estimation using a novel seaweed-attached sensor

The growth rate of seaweed is significantly affected by wave parameters and sea conditions. The wave characteristics in an aquaculture farm is normally measured using expensive equipment, which is not affordable for many farmers or researchers, and is not easily relocated from place to place to evaluate wave conditions in a variety of locations. In this paper, a sensor fusion method is presented which can estimate wave height using the data logged by a multi modal low-cost seaweed-attached sensor system. The sensor was developed for use in an Aquaculture scenario. This method is based on combination of extended Kalman filter and artificial neural networks. Regarding the importance of studying the impact of wave on seaweeds growth rate, this method will avail many researchers to use wave height data in their study to fill the gap in knowledge of the impact of water motion on aquaculture and maximising of seaweed harvests