Predict sex in salmonids using motion-triggered cameras and artificial intelligence

As the EU water directive starts to be implemented in Swedish law a new national plan of negotiating new environmental permits for every hydropower plant. This process will most likely result in the building of many new fish passages and ladders around the dams to allow fish and other aquatic life to pass them. Those new passages will have to be evaluated to ensure high effectiveness. To accomplish all those studies a new methodology to census fish in a cost-effective and non-labour-intensive way. This project aims to develop and test a new model which can predict if a specific salmon or trout is male or female. Further on to compare the new model with already existing census methods used to study migrating species of fish. To collect the data needed for this study a camera unit developed by the company TIVA AB to count fish was placed in a salmon trap in the mouth of Umeälven near Obbola, Västerbottens län. The pictures displaying salmon and trout from the camera were then annotated in Labelstudio to have a dataset to train the model with. To build the model a pre-built algorithm called Yolov5 was used as a base. This algorithm is an improvement to previous AI-learning algorithms as it only looks at the pictures once which increases working speed in comparison to previous models which looked at every picture multiple times. The results from the two tests conducted show an accurate model when tested on data from the same camera station where light conditions and other parameters match the training data. When tested on data from another site in Stornorrfors with a different camera setup the results are not as accurate. Unfortunately, the project suffered from big data losses which made the dataset too small to build a very precise model. However, the results show that it is possible to build a model that can predict the sex of a salmon or trout. This is a step towards identifying unique individuals with the help of AI. When more extensively developed, this method wil