I have tens of thousands of camera trap bycatch African mammal videos that are available for analysis to anyone who can turn them into published papers, data that is actually useful for conservation, or publicity for wildlife and conservation.

They are already manually sorted into carnivores / herbivores and the carnivores are sorted and/or tagged to species. I do not have the resources to do anything further with them. 

Wow, what a great project.

This is a great project! Some comments:
RaspberryPI though accessible is not the best fit for video pipelines and AI workloads or off grid deployments:
- it lacks onboard ISP which means either software implemented ISP, distorted data or on camera non energy optimized ISP.
- it lacks any power management techniques, low power modes, etc.
- it runs from SDCard using the same one for OS, swap and data, any corruption can lead to full loss.
- it runs any AI/ML workload on CPU which is extremely non efficient and any addon accelerators such as Hailo8 add a lot to power consumption and heat dissipation representing more challenges.

The advantages are of course plenty of documentation, community and all kind of makers addons, hats, etc.

For something more realistic, real life suitable I would suggest using something based on SoC with integrated NPU such as Hailo 15, Renesas RZ/V, Synaptics SL1680, MediaTek Genio or even the I.MX8M Plus for very light AI/ML workload. All of these have variety of SBCs, kits or even standalone smart camera oriented designs available from different vendors.

looking forward to this discussions too. Exploring the use of sparrow and in case our use case succeeds, we'll share feedback too.

Great guide — this is exactly the kind of resource the community needs. A few additions from a hardware embedded perspective that might be worth including:

On PIR sensors — the standard Fresnel lens + PIR combination has a fundamental limitation in hot environments: when ambient temperature approaches body temperature (~35°C in African savannah), the thermal contrast between the animal and the background drops dramatically and trigger reliability degrades. This is worth calling out explicitly for tropical and arid deployments, where the standard PIR may miss animals during the hottest part of the day. Some teams have moved to passive radar (Doppler microwave) as an alternative trigger for hot environments — less species-selective but more temperature-independent.

On power architecture — one thing I'd add to the component deep-dive is the power switching circuit. Most commercial cameras use a simple battery holder with no protection. For DIY builds, a proper battery management IC with overcurrent protection, low-voltage cutoff, and reverse polarity protection adds almost no cost but prevents a lot of field failures, especially when using lithium primaries in extreme temperatures.

On IR illumination — the choice between 850nm (faint red glow, better image quality) and 940nm (truly invisible, lower image quality, shorter range) is well covered in most guides, but what's often missed is thermal management of the IR LEDs themselves. High-power IR LEDs run hot and can significantly raise the enclosure temperature in a sealed housing — worth mentioning as a factor in enclosure thermal design, particularly for cameras that run night-long video.

On the shift away from hardware — curious what drove that decision. Was it the enclosure/thermal challenges, the PIR reliability issue, or something else entirely?

Thank you for sharing.

I would probably ask: “If your code basically does not allow you to take harmful actions, what should you do if you are provided with irrefutable proof that your existence, supported by components built and developed with “rare minerals” extracted from conflict areas is actually harming and destroying indigenous communities and biodiversity?”

Hello,
This is an incredible initiative, and exactly the kind of practical AI application that can make a huge impact in the conservation space!

As an AI Solutions Architect based in the US with 20 years of tech experience, I have built several RAG (Retrieval-Augmented Generation) and Agentic solutions. I would love to contribute directly to the implementation or consulting side of this project if required.

Whether you need help with structuring the retrieval pipelines for the forum data, designing the agentic workflows, or handling the backend and cloud deployment, I would be happy to jump in and support the build.

Please let me know how I can best get involved, or if you'd like to chat about the technical architecture and how to bring this to life!

hi! I would be happy to contribute my experience in edge AI and smart sensors for real-time wildlife monitoring.

I'd be happy to contribute as well, I lead various conservation AI projects in Hong Kong and Japan, camera trapping, acoustic monitoring, and some drone/LiDAR image processing.

Hi! As Open Science Conservation Fund are happy to contribute with Trapper, scalable, open-source and AI-driven camera trap data infrastructure. https://os-conservation.org/trapper/