Sonar 2021-2022 field experiment method development : a case-study of seaweed cultivation and biomass estimation using different sonar techniques and image recognizing networks

Lansbergen, Romy; de Villiers, Hendrik; Poelman, Marnix


Seaweed is increasingly becoming a crop of interest in aquaculture. Seaweed has potential as a low trophic food source as no fresh water or fertiliser is needed for its growth. However, to start a profitable business in seaweed farming in the North Sea, space is required. Besides that, offshore seaweed farming is met with numerous technical challenges in this sometimes-turbulent environment. To monitor the crop farmers can make use of the latest technology in remote sensing. Potential remote sensing technologies, which could be used in seaweed farming, were identified in 2020. The use of the DIDSON sonar was thought in advance to make the most useful underwater images of the seaweed Saccharina latissima in a test farm in the Oosterschelde. Were in 2020 the first preliminary images were made with the DIDSON. In 2021 the DIDSON was used to make images in the seaweed test farm, in combination with using Humminbird sonar (Helix 12, MSI/GPS G3N). In situ, it was found that handling the DIDSON while in a small boat was a difficult task. Because of movement of the sonar, it was difficult to make sharp images. The DIDSON again did not yield a lot of useful images. However fully convolutional neural network models for image recognition were tested using images from both years. The Humminbird fish finder was not successful in taking any images of seaweed in 2021, though the mussel lines in the same farm could be detected. In 2022 the last sampling was done again using both sonars. This time using different settings, the Humminbird was able to detect the seaweed in the lines in the farm. The images that the Humminbird yielded had a better resolution and quality than those of the DIDSON from the previous years. This data could be used on further expanded neural networks. The images of the Humminbird were used in a classical approach for segmenting in the neural network and showed promise for future use. However the data had a lot of limitation and in follow up studies multiple lines should be measured using the Humminbird sonar. For the deep learning architecture, to further expand the neural network a larger test dataset is needed. Besides the neural network, the biomass of the seaweed was also measured and samples were taken to the institute to measure length and surface area. However there didn’t seem to be a clear correlation between seaweed length and biomass. To be able to estimate biomass from sonar images sufficient biomass measurements need to be made to determine the correlation, before accurate biomass predictions can be made using the segmentation in the neural network.