Depending on how much drift there is it may be a fixed offset caused by the timer not restarting until you have finished puttin gin al the settings. You set the time, then do all the the other settings for a couple of minutes, then exit settings and the timer starts from the time you set, in other words wto minute slow. The apparent drift will be short and fairly consistent, and will not increase with time (it is a bias). The solution is to leave the time setting to last and exit set p immediately after you enter the time.

If it is genuine drift then you can correct for it to an extent by noting the time on the camera and on an accurate timepiece when you retrieve the images. If you want to get fancy  you can take an image of a GPS screen with the time on it, and compare it to the time stamp on the image.

I don't have an easy solution or a specific recommendation, but I try to track all the systems that do at least one of those things here:

https://agentmorris.github.io/camera-trap-ml-survey/#camera-trap-systems-using-ml

That's a list of camera trap analysis / data management systems that use AI in some way, but in practice, just about every system available now uses AI in some way, so it's a de facto list of tools you might want to at least browse.

AFAIK there are very few tools that are all of (1) a data management system, (2) an image review platform, and (3) an offline tool.  If "no Internet access" still allows for access to a local network (e.g. WiFi within the ranger station), Camelot is a good starting point; it's designed to have an image database running on a local network.  TRAPPER has a lot of the same properties, and @ptynecki (who works on TRAPPER) is active here on WILDLABS.

Ease of use is in the eye of the beholder, but I think what you'll find is that any system that has to actually deal with shared storage will require IT expertise to configure, but systems like Camelot and TRAPPER should be very easy to use from the perspective of the typical users who are storing and reviewing images every day.

Let us know what you decide!

Hi @lucianofoglia 

Thanks for sharing your thoughts with the community. What you've touched on resonates with a number of users and developers (looking at you @Rob_Appleby) who share similar concerns and are keen to address these issues.

As a beliver in open sourcing conservation technologies, to mitigate issues you've noted (maintenance of technologies / solutions, repairability, technical assistance to name but a few), really the only way to achieve this in my eyes is through the promotion of openness to enable a wide range of both technical and non-technical users to form the pool of skills needed to react to what you have stated. If they can repair a device, or modify it easily, we can solve the waste issue and promote reusability, but first they need access to achieve this and commerical companies typically shy away from releasing designs to protect against their IP that they keep in house to sell devices / solutions. 

I would think for an organisation to achieve the same the community would need to help manufacturers and developers open and share hardware designs, software, repairability guides etc, but the reality today is as you have described.

One interesting conversation is around a kitemark, i.e a stamp of approval similar to the Open Source Hardware Association's OSHWA Certification), but as it's not always hardware related, the kitemark could cover repairability (making enclosure designs open access, or levels of openness to start to address the issue). Have a look at https://certification.oshwa.org/ for more info. I spent some time discussing an Open IoT Kitemark with http://www.designswarm.com/  back in 2020 with similar values as you have described - https://iot.london/openiot/

You may want to talk more about this at the upcoming Conservation Optimism Summit too. 

Happy to join you on your journey :)

Alasdair (Arribada)