Researchers from the University of Trento have developed a Raspberry Pi-powered device that automatically detects pests in fruit orchards so they can get sorted out before they ruin a huge amount of crop. There’s no need for farmer intervention either, saving their time as well as their harvest.
The researchers devised an embedded system that uses machine learning to process images captured inside pheromone traps. The pheromones lure the potential pests in to have their picture taken.
Each trap is built on a custom hardware platform that comprises:
- Sony IMX219 image sensor to collect images (chosen because it’s small and low-power)
- Intel Neural Compute module for machine learning optimisation
- Long-range radio chip for communication
- Solar energy-harvesting power system
The research paper mentions that Raspberry Pi 3 was chosen because it offered the best trade-off between computing capability, energy demand, and cost. However, we don’t know which Raspberry Pi 3 they used. But we’re chuffed nonetheless.
How does it work?
The Raspberry Pi computer manages the sensor, processing the captured images and transmitting them for classification.
Then the Intel Neural Compute Stick is activated to perform the machine learning task. It provides a boost to the project by reducing the inference time, so we can tell more quickly whether a potentially disruptive bug has been caught, or just a friendly bug.
In this case, it’s codling moths we want to watch out for. They are major pests to agricultural crops, mainly fruits, and they’re the reason you end up with apples that look like they’ve been feasted on by hundreds of maggots.
When this task is done manually, farmers typically check codling moth traps twice a week. But this automated system checks the pheromone traps twice every day, making it much more likely to detect an infestation before it gets out of hand.
The brains behind the project
This work was done by Andrea Albanese, Matteo Nardello and Davide Brunelli from the University of Trento. All the images used here are from the full research paper, Automated Pest Detection with DNN on the Edge for Precision Agriculture, which you can read for free.