Using an HD camera, a special lighting system, and a laser scanner, the setup can count grapes as small as 4mm in diameter, and using algorithms, is able to use the number of grapes and convert that to an estimated harvest yield. And while the margin of error is 9.8 percent, in humans, it’s 30, demonstrating that the Computer Vision system is more efficient and possibly more cost-effective.
Currently used for lettuce crops, the company’s technology learned how to recognize the plant by analyzing close to 1,000,000 photos of lettuce. When a camera relays images of weeds growing among the lettuce plants, the software instructs a mechanical knife to root them out. As a backup, the software can send a signal to a sprayer that douses the weeds with herbicide.
In a few years’ time, with technology like this, could you see yourself growing the garden of your dreams in your backyard, freed from pulling weeds?
This blog is sponsored by ImageGraphicsVideo, a company offering ComputerVision Software Development Services.
The system had its first test-run at a bakery in Tokyo, where employers are benefitting. This is because their new employees who haven’t yet learned the ropes, or part-timers who don’t know the name of every kind of baked good, can still work the cash registers. Additionally, when there are long lines, it can speed up the check-out process, making the entire operation run more efficiently and smoothly.
While the system works relatively well, there still are some kinks to work out. For example, baked goods are easily distinguish by their shapes and toppings, but when it comes to sandwiches, the machine has a tougher time telling them apart.
Luckily, there are other companies out there with the technology to build even better versions of this same sample system. For example, the people at ImageGraphicsVideo can build a similar system which also has a learning capability. This means that whoever is using the system can input, or “teach,” new items to the computer. Not only that, but the user can point out when items are incorrectly identified, which the program then learns and uses in the future to avoid making the same mistakes.