Question For Improving Custom Floating Trash Dataset For Object Detection Model

I have a dataset of 10k images for an object detection model designed to detect and predict floating trash. This model will be deployed in marine environments, such as lakes, oceans, etc. I am trying to upgrade my dataset by gathering images from different sources and datasets. I’m wondering if adding images of trash, like plastic and glass, from non-marine environments (such as land-based or non-floating images) will affect my model’s precision. Since the model will primarily be used on a boat in water, could this introduce any potential problems? Any suggestions or tips would be greatly appreciated.