Estimating body weight of caged sea cucumbers (Apostichopus japonicus) using an underwater time-lapse camera and image analysis by semantic segmentation

Abstract

Image analysis is being developed to improve the efficiency of fishery and aquaculture technologies. Optical cameras are an easy and cost-effective method for monitoring fish and other species. In this study, a monitoring system that combines an underwater time-lapse camera and a deep learning-based image analysis was developed for utilization in integrated multi-trophic aquaculture (IMTA). The sea cucumber (Apostichopus japonicus) was used as a target species because the technology necessary for estimating growth, particularly in terms of weight, of caged sea cucumber using an underwater environment is still under study. Therefore, semantic segmentation was applied to classify the images into caged sea cucumbers and various underwater backgrounds. Multiple images of sea cucumbers were captured in a water tank that mimicked the box cage used in IMTA, and their body weights were measured simultaneously. For model development, approximately 1,300 images were prepared for the training and validation processes. The model then achieved an IoU (Intersection over Union) of approximately 94 % for the validation data. Next, the pixel numbers of sea cucumbers were converted into an area calculated using the size of the cage net as the background. The relationship between the area and weight of sea cucumbers yielded an approximate line for estimating body weight. As a result, the approximation line had a coefficient of determination of R2 = 0.87 for training and validation data and RMSE (Root Mean Square Error) =1.81 and 6.78 g for sea cucumbers less than 10 and 110 g, respectively. Using the model, test images in an actual IMTA situation were applied, and the estimated body weights were close to the measured values for small sea cucumbers. If we apply this model to images obtained over an extended period, the growth of sea cucumbers in a time series can be understood.