Marijuana Business Magazine January 2020

Marijuana Business Magazine | January 2020 70 allows growers to change production methods based on their desired outcome for the crop. “You can try to elongate or compress certain stages of basic growth depending on what attributes or what key expression or what type of compounds you want to pull the most data,” Greenberg said. “For example, certain growth stages directly correlate to more dry weight, and as you compress or elongate them and keep them in different phases, (it) could result in more dry weight but reduce your extract value.” While growers could track plant growth manually with graphical tracking, it’s time-consuming and labor-intensive, Greenberg said. “This allows you to see in a more granular way and unlocks the ability to change aspects in your growth cycle that you normally wouldn’t be able to see with the naked eye.” Moreover, tracking plant growth and comparing growing systems can help cultivators save money. For example, Keiller said one cannabis grower used an Autogrow system to compare different cultivation environments to find out which was most efficient. The operation grew the same cultivar in an indoor warehouse, a greenhouse, a shipping container, a high-tunnel greenhouse and an open field. “We ran it all and looked at data ana- lytics and growing methods and what was producing more and why,” Keiller said. “It would be very hard for a grower—apart from absolute yield and their sense of the quality of the crop—to make that determi- nation by themselves. They’d need a fleet of scientists. Most growers can’t afford to hire scientists to be part of their operation, so how else do you provide those services? You do it through AI-based tools. You’ll see more and more coming out in the industry—new emergent applications that weren’t there before in order to account for that type of expertise.” YIELD FORECASTING AND INVENTORY By combining image processing through machine or computer vision with AI, systems such as FarmRoad from Autogrow and Luna from iUNU can count flowers and predict yield, giving growers a better idea of what to expect from their crops, Keiller said. Further, data science applied through AI can compare yield data to environmental data to determine what might have gone right or wrong during the season, so growers can learn best practices going forward. “This is an example of applied AI—a grower could never figure it out six ways from Sunday,” Keiller said. “You’d have to apply data science to get right down to the specifics.” One of the most exciting applications of computer vision technology for owners of grow operations has been the ability to track plant inventory throughout the facility, Greenberg said. “They have a map of where everything is in the facility and the status of it,” Greenberg said. “We’re all visual creatures in this industry. If you’re asked what’s ready, you have to go put a height on it, so we transitioned to a vision-first, map-first methodology over the past six to 12 months.” Cannabis growers can save time and money using machine vision- and artificial intelligence-enabled systems that capture real-time production information, helping them identify and solve plant health issues immediately, whether remotely or on-site. Photo Courtesy of iUNU Laura Drotleff writes about hemp for Hemp Industry Daily and Marijuana Business Magazine. She can be reached at laurad@ hempindustrydaily.com.