Developing an AI-Powered Wildlife Monitoring Platform

One of the major challenges was to make the hardware setup simple for non-technical users. Manual calibration and technical installation are major hurdles to the adoption and scaling of most wildlife-monitoring systems. The client requested a setup time of under 10 minutes for the clients to start up their own monitoring stations without engineering support.One of the other challenges was live environmental audio. Often, wind, rain, insects, backscatter from animals, and other outdoor sounds can cause AI detection accuracy to drop, depending on the type of recording. The system had to analyze continuous audio data while simultaneously being able to reliably identify wildlife.

There was also a need to synchronize real-time information between monitoring stations, cloud, mobile apps, and web dashboards. Audio streams, species detections, and sensor data to maintain up to date without loss of sessions/stoppage time.With more stations connected, scalability became another issue as well. The infrastructure needs to scale as more AI processing workloads are placed on top of it, and the performance needs to be unaffected.

We have developed the mobile application to add to the live monitoring of wildlife, device management, and live audio streaming capabilities on iOS and Android devices. The website and product management experience used Shopify to facilitate the purchase and activation of monitoring stations by users.In the backend, we employed Python and FastAPI for API management, live audio processing, device control, and synchronization between monitoring stations and user dashboards, achieving a seamless and efficient data flow. The primary data store for wildlife detections, activity, user information, and monitoring logs was PostgreSQL.

To enable species recognition using AI, we adapted BirdNET models that can accurately identify thousands of bird species as they occur in the audio environment, in real time. We also added audio-filtering and noise-reduction workflows, which enhanced the reliability of the detection outdoors in wind, rain, and overlapping sound.Live streaming, storage, and scalable processing workloads for connected monitoring stations were provided by AWS cloud infrastructure. The platform had been built to accommodate the increasing number of deployed devices with no impact on monitoring or live platform synchronization.

We added QR device activation for faster device onboarding to enhance the user experience. We also created a live monitoring dashboard and global listening map, which enabled users to investigate what was happening around them, in terms of wildlife activity, species detections, and linked monitoring stations around the globe.

Provided a fully fledged AI empowered wildlife monitoring system comprising a mobile application, a Shopify-based website, wireless monitoring equipment, and cloud functionality that allows for real-time monitoring of the environment.

Key deliverables included:

• Live Wildlife Sound Distribution System.
• This is an AI-based bird species recognition system called BirdNET.
• Control devices and improve the experience of live listening with a mobile app.
• Web-based dashboard to observe activity and wildlife data.
• QR Code Workflows for Station Set-Up & Activation.
• Cloud-based sync for connected monitoring stations.
• A worldwide listening map to explore live stations around the world.
• The support for the integration of environmental sensors.

The platform has simplified onboarding and QR activation flows to cut down nearly 25-30 minutes to less than 10 minutes average device setup time. During the test, the accuracy of wildlife detection was improved by about 18–25% with AI optimization and audio-filtering improvements.

Infrastructure enhancements also lowered the number of interruptions to live streams from approximately 8-10% to less than 3%, providing more consistent live monitoring user experience at a variety of locations. Researchers were able to access real-time environmental data more quickly, and non-technical users were able to quickly deploy and manage monitoring stations with little assistance.

The platform was a good success in making wildlife monitoring connected and accessible. Users can easily deploy monitoring stations, listen to real-time ambient sounds, and automatically identify wildlife using AI analysis.

The solution enhanced the workflows of tracking biodiversity and overcame the technical challenges typically blocking the way to large-scale environmental monitoring projects. Researchers were able to have quicker access to real-time wildlife data, while hobbyists and educators have made it easier to be involved in conservation.The project also showcased Coding Crafts' capabilities in developing robust and scalable AI-driven platforms that integrate hardware components, real-time processing, cloud systems, and user-centric applications.