A “Vision” for Craft Cannabis
Plants are organisms that move much like you or I do, but on a different time scale. Growth occurs so slowly that we don’t usually stick around long enough to notice the changes. But if we can take modern cameras and set them on timers to dutifully document the progress, we can record plant growth over time lapse videos.
Putting equipment on timers is nothing new and generally doesn’t require the flexibility of a computer. However, computers have demonstrated the ability to perform much more sophisticated tasks that would otherwise require human intervention. For example, today in 2018, cars are taking their first steps towards being able to drive themselves, phone can now recognize your face, and computers can categorize images by their content. It’s incumbent upon those of us who want to accomplish more with less, to adapt new technology to suit our needs.
We Garden Visually
At first glance, timers seem to be the most logical way to program your grows. After all, when everything is dialed in, your grow proceeds like clockwork. Timers handle the tedium of many small chores, freeing our time up for the higher level task of diagnosing the occasional problem.
But failures will inevitably occur. Thinking back to the last time this happened to you, you probably spotted something that didn’t quite look right. Humans are primarily visual creatures and plants typically reveal their status visually and chemically. And so, it is only natural that we should consider systems which incorporate the visual context in performing actions or relating information.
If computers are to aid us, they must perform some task or gather information pertinent to the user that frees their time up to perform higher level thinking. Imagine an irrigation system that responds dynamically to the visual feedback of your plants. Such a system reduces waste by eliminating overwatering and underwatering. Or a computer system could notify you about nutritional deficiencies, pests, or a mechanical failure on your air conditioning, allowing you to react quickly before the damage gets worse.
In your breeding programs, you may want to select for something other than THC production. Imagine a camera that could track, document and sort your plants by phenotype expression. Just as devices like Nest are designed to learn your preferences for home temperature control and send security alerts, it is now possible to build environmental controllers that integrate visual and environmental context with domain expertise to reliably produce the desired conditions.
Home Automation, Meet Home Grow!
With Kindbot, we tap into the same home automation and IoT advances which have dramatically reduced the cost to setting up a connected home. We apply these new technologies in order to produce quality cannabis. Kindbot takes measurements of the grow environment, along with camera information, in order to extract valuable information and make logical decisions, activating or deactivating different equipment.
We’ve optimized Artificial Intelligence object detection models to parse the scene of typical cannabis grow spaces. This means that Kindbot understands different nutrient deficiencies and recognizes flowers for quick yield estimation. It can even can sex your preflowers and read the buds to recommend harvest times.
Scaling Craft Cannabis
Few plants receive the fuss and scrutiny of cannabis, which is why it makes sense for us to go the extra mile in applying any useful tools to produce the best buds. The cannabis plant is amazingly robust, yet vigorous growth isn’t necessarily the objective.
We envision an opt-in information-sharing cooperative where, through the course of many grows, both the Kindbot team and others are able to draw upon the results of the community to optimize conditions for popular strains, perhaps setting new records. This is a community effort. We built Kindbot using open source on the world’s greatest open-source medicine. As the open-source technologies mature, Kindbot will inherit the progress.
Contact us to learn more about the Kindbot Collectif.
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About the Author
After brief stints fishing King Crab in AK and building surfboards in HI, Terry Rodriguez trained in mathematics at UNC-Chapel Hill and UC Berkeley where he met musician and fellow mathematician Salma Mayorquin. Salma and Terry have joined forces to develop software for large healthcare providers and taken recent wins in hardware hacking challenges. Now, they apply their software/hardware chops to hack next generation smart IoT products with the dream of bringing affordable, scalable grow room automation hubs to the world.