Happy new year everyone playing with deep learning ag robots!
Thanks for the link Aidan… but it’s a tease because no serious model details are discussed 
It’d be interesting to know network architecture they’re using e.g. plain Mask RCNN or something custom.
I didn’t make mega progress (usual lack of time excuse!) and it was mostly on the robot rather than deep learning (i.e. not so interesting for this forum).
- Robot improvements- alu frame, chain drive, 2*12ah AGM batteries (only 1 fitted currently), temporary mast and boom for camera, rear jockey pivot has proper bearings, …
The main problem is that it throws the drive chain on bumps- needs better tension and alignment.
Generally everything needs improvement before running it in the field. - Jetson Nano now has a little SSD1306 OLED screen to show model output and other useful stats ( useful ref: https://github.com/JetsonHacksNano/installPiOLED )
The nano lives in the ice cream tub on the mast. At the moment it’s totally independent of the pixhawk (incl. power). Hopefully getting them talking with dronekit/mavlink will be straightforwards. Currently when the robot starts I SSH in (with WiFi) and run the capture/model script inscreen.
I hope LoRa WiFi will reach the initial target fields and 4G dongle won’t be necessary. - For the modelling part the vague idea is:
a) do an early pass with the robot just to gather lots of data (no spraying)
b) build features via self supervision
c) use those to train a preliminary classification model based on the existing tagged data
d) tag a small sample from new 2021 data (trying to ensuring key features are represented) for fine tuning and testing
Generally I’m hoping to avoid tagging lots of new data… any tips welcome!