Hi I’m looking to train a weed detector to detect specific weeds I would like to spray in my grassland fields. Im a hay farmer and the alternative is to hand spray the weeds over hundreds of acres or spray large ares with a tractor mounted sprayer. Both options are time consuming and the tractor uses significantly more chemical. I have a video of what I have managed so far using Tensorflow.
I retrained this Tensorflow model starting from the ssd_mobilenet_v1_coco_11_06_2017. My training data was pictures I have taken myself of the actual weeds in the actual field I am planning to test the machine on.
I used a tutorial by sentdex on youtube and I was surprised I got it working at all. (cpu training on a iMac)
My problem is the model does not train well I am struggling to get the total loss below 2. I plan to watch the fast ai tutorials and try and improve my detection.
Things I am thinking about.
- Choosing a different model to retrain that might transfer to grass field weeds detection better.
- Training for much longer I am at about 5000k steps
- My bonding boxes take in too much of the grass in the back ground and no enough just weed? Could I use segmentation to only capture the weeds??
If anyone wants to join the project I am calling it opensprayer.com and the end goal is too build a autonomous grassland sprayer for a total materials build cost of £2000. Hopefully we will soon see some small boards in the future like Arduino that can run neural nets cheaply and fast. Other than the cost of the compute I think this may be possible. I would like to see some practical ai projects that don’t cost the world but still do useful work and act on the physical world.
Ideally I would not be training the model but instead maybe just building the hardware and collecting the data. In the meantime I am keen to learn about training the best model I can