M-Count: an application that uses machine learning object detection and color thresholding to count settled mussel larvae

Key Points

• This research aims to develop an application that improves the counting accuracy and efficiency of mussel larvae in biofouling assessments.
• Developed M-Count application utilizing machine learning techniques.
• Implemented object detection algorithms to identify mussel larvae.
• Applied color thresholding methods for enhanced visibility of larvae.
• M-Count achieved counting accuracy comparable to human observers.
• Significantly reduced time required to quantify mussel larvae on test surfaces.