Fish Density Estimating Boat


  • Kavitha Issac
  • Neetha John
  • Asnil Arif
  • Revathy M U
  • Rohit M Nair
  • Thejas Sebastian Sunny


Raspberry Pi, GPS, Object Detection, Image Analysis Algorithm.


Object counting in aquaculture is an important task, and has been widely applied in fish population estimation. With the rapid development of sensor, computer vision, and acoustic technologies, advanced and efficient counting methods are available in aquaculture. The Fish Counting Boat integrates raspberry pi as the central controller alongside a USB camera, GPS for location tracking, and dual motors for propulsion. This innovative setup enables efficient fish counting and tracking in aquatic environments. Leveraging machine learning techniques in the software component, the system automates fish detection and counting processes, enhancing accuracy and reliability. By utilizing image analysis algorithms, the USB camera captures real-time footage of the surrounding underwater environment, enabling the identification and enumeration of fish species. To create a prototype of a boat with the developed software that has features for remote work also in the variety of harsh weather and environmental conditions, which can be remotely controlled using Internet. Designed system enables fish spatial location (GPS coordinates). In order to detect the presence of fish, several machine learning techniques are used. Object detection with the help of TensorFlow and YOLO have been used. The measurement method for estimating the fish density using the boat is based on a sequential calculation of the number of occurrences of fish on the set trajectory.


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How to Cite

Kavitha Issac, Neetha John, Asnil Arif, Revathy M U, Rohit M Nair, & Thejas Sebastian Sunny. (2024). Fish Density Estimating Boat. International Journal of Progressive Research in Science and Engineering, 5(05), 76–79. Retrieved from