Hi Andrew,
this is great! 
Did you consider the Witty Pi 4 L3V7 Real Time Clock (RTC) und Power Management Modul für Raspberry Pi?
(This one has no included battery) or the one with the capacitor?

Greetings from Austria,
Robin

A fantastic solution @hikinghack ! Those Pijuice boards are more expensive than the pis themselves! 

It already sounds as if you've gotten the power use in sleep down a long way, but I thought I'd mention the Adafruit TPL5110 timer boards. They get draw down to around 20uA. They are limited to a 2-hour max interval so won't be useful for applications that only wake up once or twice a day etc., unless you write a script with something like a counter to count off multiples of wake up times. They are also controlled by resistors, so the timing won't always be perfect. When it's finished, our TimeLord board will allow a lot more sophisticated scheduling, be precise and I am pretty confident it won't cost anything like the juice! If you'd like to take some for a spin when they are ready just let me know (and same for anybody else).

I really like that pis and some Arduino's are including RTCs now (although the Arduino R4 internal RTC is crystal-less so might not be super accurate, not sure about the Giga)...makes things that little bit easier and less expensive.

Well done on the great solve!

Cheers,

Rob

Already an update from @hikinghack: 

And finally for now, the object detectors are wrapped by a python websocket network wrapper to make it easy for the system to use different types of object detectors. Usually, it's about 1/2 a day for me to write a new python wrapper for a new object detector type. You just need to wrap in the network connection and make it conform to the yolo way of expressing the hits, i.e. the json format that yolo outputs with bounding boxes, class names and confidence level.

What's more, you can even use multiple object detector models in different parts of a single captured image and you can cascade the logic to require multiple object detectors to match for example, or a choice from different object detectors.

It's the perfect anti-poaching system (If I say so myself :) )

Hi all! I'm part of a Pollinator Monitoring Program at California State University, San Marcos which was started by a colleague lecturer of mine who was interested in learning more about the efficacy of pollinator gardens. It grew to include comparing local natural habitat of the Coastal Sage Scrub and I was initially brought on board to assist with data analysis, data management, etc. We then pivoted to the idea of using camera traps and AI for insect detection in place of the in-person monitoring approach (for increasing data and adding a cool tech angle to the effort, given it is of interest to local community partners that have pollinator gardens). 

The group heavily involves students as researchers, and they are instrumental to the projects. We have settled on a combination of video footage and development of deep neural networks using the cloud-hosted video track detection tool, VIAME (developed by Kitware for NOAA Fisheries originally for fish track detection). Students built our first two PICTs (low-cost camera traps), and annotated the data from our pilot study that we are currently starting the process of network development for. Here's a cool pic of the easy-to-use interface that students use when annotating data: 

                                                    Figure 1: VIAME software demonstrating annotation of the track of an insect in the video (red box). Annotations are                                                           done manually to develop a neural network for the automated processing.

The goal of the group's camera trap team is develop a neural network that can track insect pollinators associated with a wide variety of plants, and to use this information to collect large datasets to better understand the pollinator occurrence and activities with local habitats. This ultimately relates to native habitat health and can be used for long-term tracking of changes in the ecosystem, with the idea that knowledge of pollinators may inform resources and conservation managers, as well as local organizations in their land use practices. We ultimately are interested in working with the Kitware folks further to not only develop a robust network (and share broadly of course!), but also to customize the data extraction from automated tracks to include automated species/species group identification and information on interaction rate by those pollinators. We would love any suggestions for appropriate proposals to apply to, as well as any information/suggestions regarding the PICT camera or suggestions on methods. We are looking to include night time data collection at some point as well and are aware the near infrared is advised, but would appreciate any thoughts/advice on that avenue as well. 

We will of course post when we have more results and look forward to hearing more about all the interesting projects happening in this space!

Cheers, 
Liz Ferguson