Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.

In video-based fish surveys, species recognition plays a vital role in stock assessments, ecosystem analysis, production management, and protection of endangered species. However, implementing fish species detection algorithms in underwater environments presents significant challenges due to factors such as varying lighting conditions, water turbidity, and the diverse appearances of fish species. In this work, a transformer-enhanced YOLOv8 (YOLOv8-TF) is proposed for underwater fish species recognition. The YOLOv8-TF enhances the performance of YOLOv8 by adjusting depth scales, incorporating a transformer block into the backbone and neck, and introducing a class-aware loss function to address class imbalance in the dataset. The class-aware loss considers the count of instances within each species and assigns a higher weight to species with fewer instances. This approach enables fish species recognition through object detection, encompassing the classification of each fish species and localization to estimate their position and size within an image. Experiments were conducted using the 2021 Southeast Area Monitoring and Assessment Program (SEAMAPD21) dataset, a detailed and extensive reef fish dataset from the Gulf of Mexico. The experimental results on SEAMAPD21 demonstrate that the YOLOv8-TF model, with a mean Average Precision (mAP)_0.5 of 87.9% and mAP_0.5–0.95 of 61.2%, achieves better detection results for underwater fish species recognition compared to state-of-the-art YOLO models. Additionally, experimental results on the publicly available datasets, such as Pascal VOC and MS COCO datasets demonstrate that the model outperforms existing approaches.

Increased necessity to monitor vital fish habitat has resulted in proliferation of camera-based observation methods and advancements in camera and processing technology. Automated image analysis through computer vision algorithms has emerged as a tool for fisheries to address big data needs, reduce human intervention, lower costs, and improve timeliness. Models have been developed in this study with the goal to implement such automated image analysis for commercially important Gulf of Mexico fish species and habitats. Further, this study proposes adapting comparative otolith aging methods and metrics for gauging model performance by comparing automated counts to validation set counts in addition to traditional metrics used to gauge AI/ML model performance (such as mean average precision - mAP). To evaluate model performance we calculated percent of stations matching ground-truthed counts, ratios of false-positive/negative detections, and coefficient of variation (CV) for each species over a range of filtered outputs using model generated confidence thresholds (CTs) for each detected and classified fish. Model performance generally improved with increased annotations per species, and false-positive detections were greatly reduced with a second iteration of model training. For all species and model combinations, false-positives were easily identified and removed by increasing the CT to classify more restrictively. Issues with occluded fish images and reduced performance were most prevalent for schooling species, whereas for other species lack of training data was likely limiting. For 23 of the examined species, only 7 achieved a CV less than 25%. Thus, for most species, improvements to the training library will be needed and next steps will include a queried learning approach to bring balance to the models and focus during training. Importantly, for select species such as Red Snapper (Lutjanus campechanus) current models are sufficiently precise to begin utilization to filter videos for automated, versus fully manual processing. The adaption of the otolith aging QA/QC process for this process is a first step towards giving researchers the ability to track model performance through time, thereby giving researchers who engage with the models, raw data, and derived products confidence in analyses and resultant management decisions.

Primary consumers of microplastics are often zooplankton species such as the mysid shrimp, Americamysis bahia. Ingesting and interacting with these plastics can cause stress and lead to death. In the presence of some environmental stressors, gene expression may be altered without changing DNA sequences via the epigenetic methylation of the DNA. Mysid shrimp were exposed to 5-micrometer fluorescent polystyrene microbeads at different concentrations and different lengths of time. No significant effects were observed on mortality within 72 h, but mortality increased significantly thereafter. Microplastics were consumed by mysids and adhered to the mysid carapace and appendages. An ELISA-like (Enzyme-Linked Imuunosorbent Assay) colorimetric assay was employed to assess mysid DNA for differences in global percent methylation. No significant difference in the average percent methylated DNA nor difference in the number of methylation detections between treatments was found. This is one of few studies that has investigated DNA methylation effects due to microplastics-induced stress and the first study to detect DNA methylation in any member of the order Mysida.