Low turbidity in recirculating aquaculture systems (RAS) reduces feeding behavior and increases stress-related physiological parameters in pikeperch (Sander lucioperca) during grow-out

There is a tendency to farm fish in low turbidity water when production takes place in the land-based recirculating aquaculture systems (RAS). However, the effect of water turbidity on stress and performance is unknown for many species cultured in RAS. The effect of different turbidity treatments as Formazine Attenuation Units (0 FAU, 15 FAU, and 38 FAU) on feed intake performance (latency, total feeding time, and total feed intake) and physiological blood stress parameters (cortisol, lactate, and glucose) in medium-sized pikeperch ((Sander lucioperca) n = 27, undetermined sex and age) of initial body weights of 508.13 g ± 83 g (at FAU 0, 15, and 38, respectively) was investigated. The rearing system consisted of 9 rectangular tanks (200 L per tank). Fish were housed individually (n = 1, per tank, n replicates per treatment = 9). All tanks were connected to a recirculation system equipped with a moving bed biofilter. Feed intake in pikeperch kept at low turbidity (0 FAU) was 25% lower than pikeperch kept at high turbidity (38 FAU) (P < 0.01) and also significantly (10.5%) lower compared to feed intake in pikeperch kept at intermediate turbidity (15 FAU) (P < 0.01 for 0 FAU vs. 15 FAU, feed intake sign. Value as the main effect is P < 0.01). Pikeperch kept at low turbidity showed significantly slower feeding response (latency time) towards pellets entering the tank, shorter feeding times (both P < 0.05), and higher glucose blood concentration (73%) in contrast to pikeperch kept at highest turbidity. A reduction of 25% feed intake has obvious economic consequences for any fish farm and present data strongly emphasize the importance of considering the species-specific biology in future RAS farming

New microalgae media formulated with completely recycled phosphorus originating from agricultural sidestreams

This study investigated the impact of struvite as a sustainable phosphorus source on the growth and phycocyanin production by the blue-green alga Arthrospira platensis. Three modified growth media were compared to the typical SAG-spirul culture media. CS(+) refers to the completely recycled struvite from bovine urine as a phosphate source, while S(-) and S(+) refer to commercially available struvite as a phosphate source. On media with (+), a pre-treatment was conducted to evaporate NH4, as it negatively affects cell growth and functions of the photosynthetic apparatus at high concentrations, and to release phosphate due to the low solubility of struvite in water. For each medium, three cultures were cultivated in Erlenmeyer flasks for a duration of 42 days. Results showed that no statistically significant negative effect of struvite was found on the growth rates. However, C-phycocyanin (CPC-P) in CS(+) and S(+) was significantly higher compared to CPC-P in untreated growth media. The study hypothesized that low concentrations of NH3 remaining after the pre-treatment of struvite could have a positive impact on phycocyanin accumulation, as an energy efficient and quick nitrogen source for A. platensis

Robust deep learning based shrimp counting in an industrial farm setting

Shrimp production is one of the fastest growing sectors in the aquaculture industry. Despite extensive research in recent years, stocking densities in shrimp systems still depend on manual sampling which is neither time nor cost efficient and additionally challenges shrimp welfare. This paper compares the performance of automatic shrimp counting solutions for commercial Recirculating Aquaculture System (RAS) based farming systems, using eight Deep Learning based methods. The entire dataset includes 1379 images of shrimps in RAS farming tanks, taken at a distance using an iPhone 11 mini. These were manually annotated, with bounding boxes for every clearly visible shrimp. The dataset was partitioned into training (60 %, 828 samples), validation (20 %, 276 samples) and test (20 %, 275 samples) splits for purposes of training and evaluating the models. The present work demonstrates that state-of-the-art object detection models outperform manual counting and achieve high performance across the entire production range and at various circumstances known to be challenging for object detection (dim light, overlapping and small animals, various acquisition devices and image resolutions and camera distance to object). Highest counting performance was obtained with models based on YOLOv5m6 and Faster R–CNN (as opposed to neural network autoencoder architecture to estimate a density map). The best model generalizes well on an independent test set and even shows promising performance when tested with different taxa. The model performs best at densities below 200 shrimps per image with an overall error of 5.97 %. It is assumed that this performance can be improved by increasing the dataset size, especially with images at high shrimp stocking density, and it is strongly believed that a performance below the 5 % error threshold is close to being achieved, which will allow for deployment of the model in an industrial setting