What AI Gets Wrong About Cannabis Lighting (and Growing)
- Posted on
- by Fluence Bioengineering
Table of Contents
Working in this industry, I’ve had the opportunity to learn from some of the most experienced growers and researchers out there who combine science, instinct, and hard-earned insights to run high-performing facilities.
As artificial intelligence (AI) continues to show up in cannabis conversations, I’ve been watching closely. The potential is real—but so are the limitations. Especially when it comes to lighting. AI is good at patterns. But cannabis isn’t a standard crop. And lighting isn’t a one-size-fits-all input.
Here are five things AI consistently gets wrong about cannabis lighting and growing—based on what we’ve learned from real-world growers and our team’s work at Fluence.
1. Oversimplifying Light Requirements
What AI gets wrong:
“Run 18/6 for veg. Flip to 12/12 for flower. Use any full-spectrum light.”What growers know:
Light schedules are only part of the equation. What matters just as much—if not more—is intensity, spectrum strategy, distribution, and uniformity across the canopy.
AI tends to treat cannabis like it treats most plants: follow the schedule, check the box. But growers know light drives plant architecture, yield, and quality. Spectrum choices impact everything from internodal spacing to chemovar expression. Intensity and uniformity shape how consistent those results are. And how you steer with light across different stages of the crop cycle? That’s where the real performance gains come from.
2. Misunderstanding Nutrient Needs and Feeding Schedules
What AI gets wrong:
“Use high nitrogen in veg, high phosphorus in flower.”What growers know:
Feeding programs aren’t templates—they’re tailored. AI often recommends nutrient strategies that sound plausible on paper but ignore critical variables: cultivar behavior, growing media, water chemistry, and system type (like drain-to-waste vs. recirculating). A well-designed feed chart works for your genetics, your environment, and your goals. It responds to the plant in real time. Generic recommendations can’t account for subtle shifts in EC or how one cultivar might demand extra calcium while another burns at the same dose. Experienced growers spot those differences before AI would even flag a change.3. Confusing Indoor and Greenhouse Environments
What AI gets wrong:
“Maintain optimal VPD and CO₂ levels. Use supplemental lighting if needed.”What growers know:
Indoor and greenhouse environments aren’t interchangeable—they’re fundamentally different operating models.
Indoors, growers can control every aspect of the environment. In greenhouses, you’re adapting to the natural rhythm of the sun. That means fluctuating DLI, daily cloud cover, seasonal shifts in photoperiod—all of which influence how and when you supplement with artificial light.
AI often lumps the two environments together when giving advice. But optimizing light in a sealed room vs. a light-assisted greenhouse requires completely different thinking, tools, and strategies.
4. Ignoring Cultivar Differences
What AI gets wrong:
“All cannabis strains flower in 8–9 weeks. Train early. Flip at 18 inches.”What growers know:
Every cultivar is different. Some stretch dramatically under flower lighting. Others stay compact. Some finish in under 8 weeks, while others push 12. The way a cultivar responds to spectrum, intensity, and training methods isn’t predictable unless you’ve grown it—or worked with someone who has.
Lighting needs to be tailored to the crop’s genetic behavior. Applying a standard approach to light strategies across all genetics limits your results. Great growers don’t treat every plant the same, and lighting recommendations shouldn’t either.
5. Overhyping Automation & AI Tools
What AI gets wrong:
“Let AI manage your grow. It can predict yield, detect pests, and optimize performance automatically.”What growers know:
Automation can absolutely help—but it’s not a replacement for real experience. Some AI tools claim to replace human oversight with predictive analytics or image recognition. But seasoned growers know how quickly an environment can shift, how subtle early signs of stress can be, and how important it is to read your crop in real time.
Lighting, in particular, requires ongoing attention. You don’t just “set and forget” your PPFD levels or spectrum mix. You monitor, adjust, and adapt—based on the plant’s response, facility data, and production targets. AI can support that process, but it can’t lead it.
Conclusion
AI is evolving fast, and it will continue to be a part of the cannabis industry. But right now, it’s not a shortcut to better yields or better plants. It’s a tool—one that works best when paired with real-world expertise, crop-specific insights, and a lighting strategy grounded in plant science.
At Fluence, we believe in equipping growers with technology that supports decision-making, not oversimplifies it. Because in this industry, nuance matters. And growers who understand their plants will always outperform software that doesn’t.
