Data-driven growing: 5 types of hydroponics data you can use to improve growing conditions and outcomes

big (grow) data

A large part of the gro.io platform centers on the collection, sharing and analysis of growing data; because we are data-driven, we  plan to create a series of blog posts that encourage conversations relating to this topic. Today, we’re kicking off this series and discussing some potential benefits of data-driven growing for the hydroponics industry.

Most people wouldn’t associate the terms “Data Analysis” or “Big Data” with growing plants, but we certainly do since we consider them both to be important aspects of hydroponic growing. There’s a reason NASA uses hydroponics for growing in space; unlike growing in soil, hydroponics is a science with very defined and controllable variables.

When approached programmatically, the process and related data of growing plants hydroponically can be broken into these major categories:

Plant / Water / Air / Light

We’ll dive into the details of each category, including their numerous sub-categories (ph, nutrients, spectrum, strain, and so on) in later posts. But for now, we want to look at how we can collect the relative hydroponics data and what we can do with it.

Auto Data Collection

Collecting clean and useful data is typically the most challenging part of any analysis effort, and so we’re very fortunate to have a platform that was designed from the start with this goal in mind. Having a wide array of system-connected sensors (plus the ability to add more) that are driven by a powerful Linux-based computer and fed into an integrated database provides us the unique ability to collect as much hydroponics data as we deem useful across all major categories. By default, the system is set to poll the available sensors every 30 minutes, but even that number is just a variable we can adjust if the community determines that more or less is better.

Program Data

While this sensor data is essential, two other areas of data combine to make it far more useful. The first is what we’ll call process or program data. These are the system level processes that define a growing program. It’s useful to know that your pH level is high, but it’s far more valuable if you know what the system has done recently that may have contributed to this. Were nutrients recently added that may have driven up the pH? Was fresh tap water just added that causes it to elevate? When was the last time the system circulated the water? Our hydroponics platform knows these things.

User Data

The next area of data that’s important is user-generated or logged data. This includes measurements and observations like plant height, leaf coloring, visible plant stress and, of course, the ultimate metrics of your final wet and dry plant yield. Currently, we don’t think the cost and complexity (not to mention accuracy) of using sensors to measure the height of your plants is beneficial; this measurement is better left to the individual grower or user.

Collaborative Data Analysis

Collectively, all this hydroponics data provides an extremely valuable and scientifically quantifiable view of your grow. This has the benefit of letting growers understand what works best as well as why things may have gone wrong. It also makes it really easy to ask for help when things do go wrong. Being able to share the complete growing program along with the detailed data log of what your pH levels have been, what nutrient dosing schedule you’re using, your water temperature… this is what other growers need in order to provide you with useful feedback and guidance.

Big Data

And finally, the bigger goal is just that: Big Data. Analysis across a few grows that all used the same program and nutrient schedule is interesting, but that same analysis across 100 or 1,000 grows is a game changer. Add to this the right level of scientific rigors, such as a subset of control grows, and you’ve got the ability to collaborate on a scale previously unheard of. These are the same types of activities that enable the technical and scientific communities to rapidly research and advance knowledge.

This is how disruption happens.

If using data for hydroponic growing intrigues you, stay tuned and make sure not to miss any future posts by subscribing to our blog.

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