10 Ways AI Is Improving Cannabis Yields And Security

• According to BDS Analytics, the Covid-19 pandemic drove retail sales up 35% above industry forecasts, accelerated by cannabis businesses being declared “essential” for medical purposes in virtually every U.S. legal market.
• Fueled by strong consumer demand, annual legal (medical and adult-use) sales are projected to grow at a compound annual growth rate (CAGR) of 21%, to reach more than $41 billion by 2025 (from $13.2 billion in 2019), according to New Frontier Data.
• BDS Analytics predicts that the U.S. Cannabis Industry will generate $20.8 billion in direct spending in 2021 and $39.6 billion in total economic contribution after factoring its indirect economic effects.

Bottom Line:  With an average yield per acre of $1.1 million, legal cannabis agriculture dwarfs all other crops in revenue potential while also providing the resources needed to fund AI-based monitoring to improve yields and security.

Cannabis’ value per acre dwarfs all other crops being produced in North America today, prompting every commercial grower to consider how they can improve yields further while securing their crops on a 24/7, virtual basis. Recent studies by the USDA, The Rand Corporation, and the Marijuana Cultivators of Oregon find that at an average price of $1,948 per pound at Colorado prices, an acre of marijuana can yield more than $1.1 million per acre. The studies compared the most widely grown crops in the U.S., including corn, soybeans, oats, and wheat, which all yield less than $1,000 per harvested acre. The following graphic from New Frontier Data illustrates how profitable an acre of marijuana is to cultivate than other crops.

Using AI to Protect & Grow a Cash Crop

AI and machine learning-based techniques based on real-time monitoring data are an integral part of today’s innovation in cannabis farm management.  Supervised machine learning algorithms capable of identifying patterns and sequences in imagery from thermal, infrared, and night vision cameras in real-time can help identify diseases affecting plants early. Identifying and alerting farm staff of a breach or break-in by an animal or person is possible using AI-based smart monitoring systems.

The more advanced a smart monitoring system is in its use of machine learning and real-time monitoring integration, the more effective it is in spotting anomalous activity.  Over time, the best AI-based remote monitoring and surveillance systems “learn” or begin to identify recurring patterns in data. Cannabis farms rely on AI and machine learning to identify which techniques for improving yield rates by specific fertilizer treatment produce the most flowers and overall yield per acre.

The following are ten ways AI is being used for improving cannabis yields and security:

• Monitoring real-time video feeds of remote cannabis fields using machine learning-based surveillance systems can identify a breach by an animal or human then send an alert immediately. Given how valuable a single acre of cannabis is to a farm, knowing in real-time if there’s been an attempted breach or break-in can save thousands of dollars in potential crop damage and theft. Federated cannabis farms with multiple remote locations are starting to use AI and machine learning-based remote monitoring to secure their operations. Machine-learning based video surveillance systems can be programmed or trained over time to identify employees versus unknown people and easily spot animals attempting to break into a field.  The following image from Twenty20 Solutions illustrates how machine learning is used for identifying activity at a remote location:

• Reducing the dependence on onsite security guards alone and gaining a 24/7, 365-day monitoring view of each grow and farm site. Instead of relying only on onsite security teams to monitor video feeds in real-time, cannabis growers turn to AI and machine learning-based surveillance to isolate the most anomalous or unexpected events given the pattern of previous activity on a site. Reducing the cost and insurance liability of having security teams on site is one of the most significant benefits of relying on a cloud-based remote monitoring system that can interpret and provide alerts based on real-time data.
• AI-based surveillance monitoring systems can prepare activity reports in minutes for state and federal auditors, saving farmers and administrative staff thousands of hours a year getting the data together for audit teams. Using machine learning and advanced video analytics, growers and their staff can prepare for state and federal audit reports in minutes instead of the many hours needed in the past.
• Helping to keep licensed cannabis growers in compliance by providing a 24/7, 90 day or longer video history of all activities at their farms keeps them in compliance with state regulatory requirements. Included in several states’ requirements are the specific requirements for video footage access, video archiving, access requirements, how cameras are placed, and how quickly video footage can be accessed. State regulatory agencies are initiating audits of licensed cannabis growing facilities in 2021. All states require video footage to be archived, yet 72% of cannabis operators fail to comply with security and surveillance requirements, according to a recent study by the Brightfield Group:
  • California regulations require that all video recordings from surveillance be saved 90 days or longer.
  • Washington requires all video recordings to be archived for a minimum of 45 days.
  • Oregon requires licensed cannabis growers to retain 24/7 video for 90 days with a minimum of 1.3mp per camera at 10fps. The exterior is 5fps.
• Cannabis farms often experiment with new fertilizers and plant treatments on a pilot acre to see if they achieve the expected results, and machine learning-based analysis of video stream data helps track results. Agricultural improvements in cannabis farming continue to accelerate as medical and leisure demand continues to grow exponentially. For example, a cannabis grower will often begin planting in the May/June timeframe to achieve a density of up to 4,000 plants per acre. Taking the real-time data stream infrared and thermal cameras of the acre will quickly tell growers how effective their new fertilizer and plan treatments are. Using the data from their monitoring system, the growers will expand the treatment to their entire farm, often over 40 to 50 acres in size.
• Monitoring every access point to a facility with video surveillance 24/7 combined with sound recording can prove invaluable in stopping a break-in before it happens. Every entrance to a cannabis farm needs to be considered a primary threat vector if the farm will stay safe. Advanced remote monitoring and surveillance systems can provide video analytics that correlates sound, video, and status of infrared and thermal cameras, which together can help identify potential break-ins. And with real-time alerts, farm staff can take action immediately even if they aren’t onsite.
• A few of the largest cannabis growing companies are experimenting with advanced video analytics combining infrared and thermal camera technologies to monitor insects and rodents’ impact on yield rates. Real-time video feeds are being digitally analyzed using advanced video analytics techniques by the largest cannabis farms today to find out how effective pesticides, insect, and rodent deterrents are at protecting their cannabis crops.
• When a surveillance system is cloud-based, it is possible to access any farm or cannabis sites’ real-time video feeds, history of alerts, and advanced video analytics from any browser-based device at any time. Remote monitoring systems that are cloud-based often provide much greater flexibility in viewing, analyzing, and sharing monitoring data than their on-premise system counterparts. Any device with a browser can access the platform’s reporting features and know what is going on at a remote farm or cannabis production facility.
• AI-based remote monitoring systems can also identify potential safety hazards to workers and reduce workplace injuries and potential liability litigation. Using advanced pattern matching supported by supervised machine learning algorithms, cannabis growers can identify when workers in high-risk roles are at risk of getting hurt while on the job. All cannabis facilities in the U.S. continue to have the requirement of everyone wearing a face shield and masks for the site to stay in compliance with CDC guidelines. Remote monitoring systems can tell immediately which work teams need coaching to remain in compliance.
• Define access privileges across a farm facility by the level of access every employee needs to do their job, which is especially useful for new hires. New hires often start in the field and don’t need access to the front offices or the accounting department, for example. One of the most challenging aspects of running a cannabis business is cash management. Using an AI-based surveillance and monitoring system integrated into the local security system and intelligent locks, employees are provided the level of access they need on the first day to be productive.