The cannabis industry is growing at an exponential rate, and, like any burgeoning industry, its means of production are starting to evolve as well. The cultivation sector of the industry has traditionally been facilitated by human hands, sans a few pieces of technology such as LED lights and basic extraction machinery. However, it appears the cannabis industry could soon see an influx in automation technology, including the possibility of harvesting robots. Soon enough, robots could replace human weed trimmers, which could lead to an even higher profit yield for cannabis companies.
Smart Robots Could Replace Human Weed Trimmers Soon
According to a report from the tech site ZDNet, one company is ahead of the curve when it comes to a fully automated cultivation system. Bloom Automation, a company whose “mission is to join intelligent automation with innovative producers, producing the next generation of cultivation,” has figured out a way to have smart robots carefully cultivate marijuana crops in a much more efficient manner than traditional human labor.
While most of the agricultural industry has largely gone to automated technology for quicker, more efficient yields, it’s been a little bit more difficult of a transition for the cannabis industry—largely in part due to the degree of difficulty and precision it takes to successfully grow high-yield, strain-specific plants.
“It’s been done by hand because the product you want is very specific,” the CEO of Bloom, Jon Gowa, said to ZDNet. “A traditional machine would chop it up. At the end of the day cultivators are selling this for quite a lot of money, so human harvesting has made sense.”
Despite the apparent need for a human touch, the company’s website says there is no major drop-off in quality when using their machinery.
“We trim with the precision of a human, but the efficiency of a machine,” the company’s website boasts.
A State Of The Art Cultivation Process
But just how will the technology work, exactly? Well, according to Gowa, the machinery will use a variety of algorithms—based off of over 6,000 plant images—to teach the robot how to separate the plant clusters. Additionally, the system will utilize a back-lit time of flight camera and a machine vision camera to further facilitate cluster identification.
Once the varying parts of the plant are identified and separated, the smart robot will then be able to accurately clip the flower.
“The system segments the plant into three parts, the flower, branch, and leaf,” explains Gowa. “We use a conventional neural network and a supervised machine learning set.”
According to Gowa, the technology has boasted a success rate of 97 percent. However, that figure is expected to increase after additional images are implemented into the robot’s database.
Down the road, Gowa expects the automation system to yield a 2-1 efficiency ratio to traditional human processing. Whether or not that exact figure will ever come to fruition, remains to be seen.