As someone who’s day job involves analytics and modeling, I’m very interested in where this conversation will lead. To @wsnook’s point, those interested in producing food can find much cheaper and easier ways of doing so. Instead, I see this project primarily as a networked experimental interface. I agree with @webbhm, this project has the potential to create a data repository of optimized recipes for growth that lead to success. Since each unit can be treated as roughly identical, our experimental sample sizes grow along with our community. Even if we all we gain are incidental insights from our logged inputs/outputs, that could still generate hypotheses that could lead to larger academic testing. Ultimately I see the insights learned here from makers/hackers/students as being applied to optimize larger growing operations that are done at scale. The sooner we can get a standardized data warehouse, the sooner we can view this project as one big network of controlled experiments.
I think of the food computer as just another appliance. I want one so I can run tested recipes that have a high probability of succeeding.
I envision people finding a recipe website with ratings, comments, success rates and animations of the plants growing. Then asking “what is this food computer?” And learning about it, purchasing one in kit or finished form (appliance) then just running it.
I agree with @wsnook, @webbhm and @will_codd that we need an open data format / plan. We will want as much data as we can collect, so it can be shared and optimized. With enough data we can use an evolutionary algorithm to find optimizations on existing recipes more quickly than doing a real life run.
To throw something out to get started…
This is a BPM/tData model hybrid of a generic growing process. It may be too abstract for what we need, as well as too detailed (ie I don’t think we will be dealing with plants that need USDA permitting to transport and plant). The rectangles are ‘fact tables’ (to use a Data Warehouse term) and the rounded boxes are activities.
Questions for going forward:
- Do we have standard protocols for planting, harvesting, making phenotypic observations (measurements)?
- What cycles and growth stages do we track (lettuce is different than corn)?
- How much detail do we want around plans, observations, etc?
- What do we have an a UI for collecting this information? Personally I think it should be in the database (Json formats?)
As an exercise, I propose we take the Cornell Hydroponic Lettuce Handbook, and the context of the Food Computer v2.0, and see what we can agree upon for data examples and models. I will start a new topic in Data for this discussion.
To clarify my position a bit, I don’t feel qualified to spend my time on data formats now. But, if other people want to work on it, that’s great.
I started this thread because I hoped to inspire more collective reflection on what we’re ultimately trying to accomplish and on what steps are required to make that happen. I wasn’t sure where I stood, and the conversation here has helped a lot in clarifying my thoughts. Thanks for all the great feedback.
I’ve concluded that I’m not ready to contribute to work on data formats because I’ve not yet grown any food hydroponically. For me, making recommendations on data formats would be talking about things I don’t understand. First, I need to meet my prerequisites–grow food.
My main goals for now are:
- Gather and share information about simple hydroponic methods to start growing food
- Start growing food and sharing updates on my progress
- Help other people start growing food
[edit: I don’t want to give the impression that I’m abandoning this topic. Rather, I think that experience growing food will help me discuss this intelligently. Right now I don’t understand clearly what data might actually matter.]
Great to read this piece. I am working on this part only, actually, although not with GAs. This will require as much data as possible to get convincing results.
Following @wsnook , collecting data on a clear scenario (e.g. hydroponics food growth) for optimization would be a terrific first stage. This would already give momentum data for algorithms to chew on. This would also check whether the community is on a good track to get more.
This is my approach with respect to optimization at this stage. But the whole thread and its breadth is really interesting. I hope such a first step can go toward the fully-fledged version envisioned by @webbhm.
For folks who haven’t seen it, the Plant Growth Chamber Handbook goes on at great length about how to record and report measurements for scientific studies involving plants in growth chambers.
I found the link on the USDA’s NCERA-101 Committee on Controlled Environment Technology and Use publications page. They also link to the International Lighting in Controlled Environments Workshop which might be useful if people want to look at recipes that involve variations in lighting.
Also, you might want to take a look at NCERA-101’s Guidelines for Measuring and Reporting Environmental Parameters for Plant Experiments in Controlled Environments. This is their background blurb from the main page:
Conditions in controlled environment plant growth rooms & chambers, greenhouses and tissue culture facilities should be reported in detail to allow for comparison of results and duplication of experiments. The guidelines presented here, including additional explanations and reporting examples, should help meet these aims and describe what is deemed the minimum required amount of information that should be gathered and reported. They also highlight parameters that could be important, but that may not have been considered for measurement and reporting.
@wsnook I don’t know how I’ve never run into NCERA-101’s publications before. I’ve seen the Plant Growth Chamber Handbook but didn’t know they had so much else.
As a note, the link you gave in the last post is returning a 404 error. I think this is the new link: