Computer Vision and machine learning help farmers kill weeds

Photo courtesy of Blue River Technology

Blue River Technology, a young startup out of Stanford University, kills weeds using Computer Vision and machine learning. In the process, farmers maximize their yield and cut back on the use of herbicides.

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.

Automated baked-goods identification can benefit businesses

Researchers at the University of Hyogo, alongside Brain Corporation, have created a computer-vision system that works to develop individual baked goods in a second.

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.

Meat slicer uses 3D scanning to improve its method

There appears to be no limit to the ways in which 3D scanning can be applied. And more recently, it has begun making its mark on the food world.

Japanese company Nantsune, which has worked in the meat slicing industry since the 1920s, has developed a meat-slicing machine that uses 3D scanning technology to more accurately cut the meat. The machine, known as the Libra 165C, was designed to work with pork, but its use can likely be extended to other types of meat.

Traditionally, the meat industry has machines which cut meat in pieces that are the same size, but because this method doesn’t account for the thickness of a piece of meat, the weight varies. After the meat is cut, it is typically weighed and packaged that way.

With the Libra 165C, the meat is scanned just before it is sliced, with the cross-section of the meat taken into account. This results in pieces that are of various thickness or size, but all weigh the same. The best part? It’s speed. The machine can cut up to 6,000 slices per hour.

The machine will retail for approximately $160,000. Do you think it will become the standard in the meat industry? Watch the video below to see for yourself how it works: