Biomass estimations of cultivated kelp using underwater RGB images from a mini-ROV and computer vision approaches

Abstract

Seaweed farming is the fastest-growing aquaculture sector worldwide. As farms continue to expand, automated methods for monitoring growth and biomass become increasingly important. Imaging techniques, such as Computer Vision (CV), which allow automatic object detection and segmentation can be used for rapid estimation of underwater kelp size. Here, we segmented in situ underwater RGB images of cultivated Saccharina latissima using CV techniques and explored pixel area as a tool for biomass estimations. Sampling consisted of underwater imaging of S. latissima hanging vertically from a cultivation line using a mini-ROV. In situ chlorophyll a concentrations and turbidity (proxies for phytoplankton and particle concentrations) were monitored for water visibility. We first compared manual length estimations of kelp individuals obtained from the images (through manual annotation using ImageJ software). Then, we applied CV methods to segment and calculate kelp area and investigated these measurements as a robust proxy for wet weight biomass. A strong positive linear correlation (r2 = 0.959) between length estimates from underwater image frames and manual measurements from the harvested kelp was observed. Using unsupervised learning algorithms, such as mean shift clustering, colour segmentation and adaptive thresholding from the OpenCV package in Python, kelp area was segmented and the number of individual pixels in the contour area was counted. A positive power relationship was found between length from manual measurements with CV-derived area (r2 = 0.808) estimated from underwater images. Likewise, CV-derived area had a positive power relationship with wet weight biomass (r² = 0.887). When removing data where visibility was poor due to high turbidity levels (mid-June), the power relationship was stronger between CV-derived area estimates and the field measurements (r² = 0.976 for wet weight biomass and r² = 0.979 for length). These results show that robust estimates of cultivated kelp biomass in situ are possible through kelp colour segmentation. However, we demonstrate that the quality of CV post-processing and accuracy of the model are highly dependent of environmental conditions (e.g. turbidity and chlorophyll a concentrations). The establishment of these technologies has the potential to offer scalability of production, efficient real-time monitoring of sea cultivation and improved yield predictions.