Humans have been consuming CBD since the prehistoric period, but it’s in recent years, with the aid of scientific research, we began to understand the diverse therapeutic benefits of CBD [1]. The demand for CBD products is soaring.
CBD exists in both the marijuana and hemp plants but is typically extracted from the hemp plant because that is the type of CBD with a legal THC amount.
Hemp growers play an essential role. They supply manufacturers with premium, organically-farmed hemp. And growers are leveraging technology, particularly artificial intelligence, to move their whole thing to the next level.
The incorporation of high-resolution sensors, automated monitoring, and management systems into hemp growing is a trail-blazing development. We will explore the various ways that AI is enhancing hemp production.
1. Precision Agriculture
Precision agriculture is one of the main advantages of incorporating AI into hemp farming. Precision farming entails the supply of all the essential factors to guarantee plant health, productivity, and efficiency.
Machine intelligence is deployed to gather critical data and test out different variables. It allows farmers to follow the best practices for planting, harvesting, and soil management.
Precision agriculture is beneficial in numerous ways including reduced costs, increased profitability, enhanced sustainability, and improved harvestability.
2. Irrigation Management
Hemp plants require a certain amount of water for optimal performance and yield. But the water needs to be free of various harmful agents that might induce nutrient deficiencies. Artificial Intelligence helps manage irrigation and ensures that hemp plants receive the proper water amount.
3. Optimizing Indoor Growing Environments
An indoor grow facility is independent of external weather. It enables you to wholly regulate your environment, including temperature, C02 levels, and humidity. Artificial Intelligence allows for active environmental control and provision of stability thanks to the ease of monitoring and collecting data.
4. Detecting and Preventing Diseases
Diseases like powdery mildew and bud rot can exert heavy losses in hemp farming. But through leveraging machine intelligence, it’s easy to monitor, prevent and eliminate disease-ridden hemp plants.
For instance, robots can detect and exterminate agents of diseases. They can also be configured to monitor and eliminate hemp plants that exhibit signs of various diseases. AI plays a critical role in the preservation of plant health and yield improvement.
5. Breeding Purposes
Hemp strains are hugely diverse, and growers face the challenge of improving the genetic properties of diverse hemp strains [2]. With machine intelligence, the procedures of observing and collecting data are streamlined, thus making it easy to create newer and superior hemp strains.
6. Grow Media Optimization
Depending on the type of your grow facility, your grow media can be anywhere from composts, soilless mixes, and hydroponics. Optimizing grow medium influences plant yield and efficiency. And machine learning may be leveraged to ensure that the growing medium is correctly set up and all the elements are present.
Soil or compost is one of the common grow media. AI can help with the analysis of various dynamics including evaporation rates, temperature, water content, and microbiome. By ensuring that the soil is in the best condition, you lay a supportive framework for outstanding crop yield and efficiency [3].
7. Harvesting
Poor harvesting techniques can affect hemp quality. With manual harvesting, the gaps for the commission of errors come up frequently, which translates into losses. But by incorporating robotics into harvesting, you minimize errors and perform a clean harvesting job within a short time frame.
Timing is an important aspect when it comes to hemp harvesting. Harvesting too early or too late can lead to resources wastage and losses.
Also, hemp harvesting involves numerous sensitive steps. With machine intelligence, it becomes easy to not only get the timing correct but to also perform these steps appropriately, thus ensuring that the hemp buds are in the best form.
8. Matching Yield with Demand
Most hemp growers are looking to make a profit. But supposing their yield is critically below or above the demand, it could potentially lead to losses. Artificial Intelligence is helpful in yield mapping and estimation, which makes for precise forecasting, thus allowing growers to meet the demand and maximize their revenue.
9. Security
Hemp growers need to protect their assets from thieves. Poor security would make it easy for burglars to burst their way into a grow facility and make away with valuable hemp resources.
Installing access control systems, surveillance, and motion sensors are critical in protecting your green resources against damage or thievery. Late-model surveillance systems make it easy to monitor your grow facility from your smartphone.
10. Minimize Expenses
The installation and maintenance costs for AI systems are no match to the cost of labor. In the long run, the use of AI systems in the management and operation of grow facilities allows growers to improve their revenue margins.
The Takeaway: AI for Hemp Growing
Hemp growers are arguably the most critical players in the CBD industry. The adoption of Artificial Intelligence into hemp growing has led to numerous benefits including improved yield, and higher profits. Machine intelligence is extremely powerful and stretches back the limits of hemp growers.
Sources:
- National Academies of Sciences, Engineering, and Medicine. (2017). Therapeutic effects of cannabis and cannabinoids. In The health effects of cannabis and cannabinoids: The current state of evidence and recommendations for research. National Academies Press (US).
- Barcaccia, G., Palumbo, F., Scariolo, F., Vannozzi, A., Borin, M., & Bona, S. (2020). Potentials and challenges of genomics for breeding cannabis cultivars. Frontiers in plant science, 11, 1472.
- Pepe, M., Hesami, M., Small, F., & Jones, A. M. P. (2021). Comparative analysis of machine learning and evolutionary optimization algorithms for precision micropropagation of cannabis Sativa: Prediction and validation of in vitro shoot growth and development based on the optimization of light and carbohydrate sources. Frontiers in Plant Science, 12.