Gramener and Microsoft AI for earth help
nisqually river foundation augment
fish identification by 73% accuracy
through deep learning AI models


Earlier, biologists would manually identify and record fish species


Now, Deep Learning AI models have been trained to detect the fish


AI-driven Image Recognition. 80% potential cost saving. Time taken reduced by 5X

A 2-minute summary of the project


The manual process to identify and name any living species through captured videos is resource intensive from a time, human, and cost perspective. So, when the Nisqually River Foundation, a Washington based nature conservation organization, encountered a similar challenge to measure and monitor the Salmon species fish identification, they approached us for an automated technology-driven solution.


First, the collected video feeds were processed to extract the relevant frames. Deep learning AI models were then trained to draw bounding boxes around each fish passing by the camera. The entire workflow encapsulated in a Web App automated the process of video feed input, detection and classification. The automated AI solution leveraged the latest implemented deep learning algorithms using the Microsoft Azure and Cognitive Services platform stack. Given the nature of the problem and the format of the video files, processing power was a key requirement for the training and validation phases. A GPU machine was the natural choice to run the object detection models. Hence, we selected the NC6 GPU VM in the Azure portal.



The Microsoft AI for Earth team was a key enabler and influencer for project’s success, through timely access for technical support and resolution of AI platform queries.

Technology Used to Resolve the Issue
  • For a reliable cloud solution with machine learning capabilities, Microsoft Azure Data Science Virtual Machine (DSVM) was chosen.
  • For the purpose of extracting frames from the videos and tag them, Microsoft Visual Object Tagging Tool (VOTT) came in handy.
  • The final object detection algorithm chosen was the YOLO V3 Video detection algorithm.
Our Contribution to Augment the Process
  • The first challenge was to process the videos and tag the fish. The heavy manual work involved in this was automated by leveraging the MS VOTT tool.
  • The tagged frames were then used to train a model using CNTK and Faster RCNN. This model was then tested against more frames extracted from the videos. While this solution was good, it lacked speed and real-time video detection capabilities.
  • As an enhancement to the solution, we moved to video object detection using YOLO V3, which provides a faster solution with real-time capabilities.

This web-based AI solution would save the client, valuable hours of expert biologist time and infrastructure costs spent in manually reviewing the videos. As part of a planned upgrade, an enhanced version of the solution has been provided to the customer, which predicts to deliver substantial cost savings to the tune of 80%.



The AI-driven image recognition solution has led to potentially saving 80% of costs.
The automated solution has also reduced the time taken to perform the species detection task by 5X.
The solution helps save the time and efforts of expert biologists who would perform this task manually earlier.

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