Our-Sci

We are building a open source reflectometer… and here’s why

We are building a open source reflectometer… and here’s why

“But wait,” you say, “there are already some out there, and they are pretty well designed and reasonably priced!”  Well, yes – there are full spectrometers like the Spectruino ($411), the Open Source Colorometer ($80 + $20 per LED) from IORodeo, and publications from universities describing open colorometer designs (Appropedia and MTU have a good one, but there are several others – these are DIY so < $100 in parts).  Pretty cheap, and lots of available designs.

Like the seat designed for the average person but usable by no one, product designers should avoid the law of averages.  As in that case, the aforementioned devices are too general purpose to be particularly useful.  The MultispeQ ($600) could work, but was designed for photosynthesis measurements and is over-designed for applications outside of photosynthesis.  For our community partners, none of these devices do exactly they need, which is…

Arborists need a low cost and easy to use chlorophyll meter to add more rigorous sensor data to visual tree assessments.

Consumers + farmers need a way to measure food nutrient density in stores and on farms.

Soil scientists and regulators need to measure soil carbon in the field, quickly and easily.  Doing so could create a massive new pathway for carbon markets to value sequestration of carbon in soil.

Cannabis growers, consumers, and dispensaries need to be able to confirm total cannabanoids and THC levels to comply with regulations, ensure quality product, and identify fraudsters.

These cases require a device which is low cost, easy to use by non-scientists, flexible in what they measure (drops of liquids, cuvettes, leaves, aggregate solids like soil, and whole solids like a pear), usable in field conditions, fast, and open source.  Reflectance is a pretty simple measurement and tells you almost nothing without reference data.  Reference data measures reflectance values and validated lab-based measurements on the same set of samples to build correlations between the two (if they exist!).  But building a reference database can be very expensive.  In the case of food nutrition, measuring a small suite of lab tests for vitamins, minerals and antioxidents can cost $500 or more.  A reference database might contain 100s or 1000s of measurements to have sufficient predictive power.  Yikes!  Expect more on solving that problem in a future post… but for now let’s just get an update on the reflectometer.

Pictures and specs

FYI – We are in the initial stages of design, so everything is in flux and I know this is ugly looking.  Sharing too much too early is in our DNA, sorry 🙂

Our core design is based on the open source MultispeQ (a photosynthesis measurement device), which uses LED supplied light sources at 10 different wavelengths, but is much lower cost.  While this isn’t a full spectrometer, it has the advantage of working independent of ambient light (unlike a normal spectrometer or simple colorimeter where the sample must be in darkness) while being relatively inexpensive (cheaper BOM and less time/cost to make/calibrate).

Ideally, we want users to be able to measure soil carbon, leaf chlorophyll content, brix from extracted sap, and the density of a pear fruit all at the same time with the same instrument.  This not only reduces the cost and increases utility, it also spreads our development costs across multiple applications.  The above design accommodates all of these uses.

This includes a digital tape measure, kind of like the Bagel.  As we validate that design more I’ll post more details.

Here is the link to the 3D design files on OnShape https://cad.onshape.com/documents/849be056da41993fee5440bf/w/fca66e62cdc277f2c4c8e7fb/e/e343e4d4ecd695e477fba916.  The hardware and firmware files will be at the Our-Sci Gitlabs page here https://gitlab.com/our-sci.  It’s a work in progress, so expect to see frequent changes over the next few months.  Much of the hardware, software and firmware has already been tested and validated, so we hope is to have a prototype device ready in only a few months.

We’ll keep the updates going between now and then, so stay tuned or sign up for email updates in the footer of this page.

Posted by gbathree
Agriculture needs an open solution stack

Agriculture needs an open solution stack

Farmers may not know about FOSS, but they know when they’re getting the short end of the stick.

There’s lots of talk about big data and tech in agriculture… and it falls into two main camps – there’s the ‘machines replace farmers’ camp, ranging from automated tractors, to automated home gardening, to home food computers.  These projects are interesting and full of promises but require something of a revolution to actually scale.  Then there’s the ‘farmers buy my black box service’ camp, a multi-billion dollar industry already providing tech services to mostly large farms – Monsanto, John Deere, DuPont all have platforms for collecting data and providing real time feedback (precision-ag, as it’s called), and there’s a huge number of startups working in the same space.  These services tend to be expensive and extremely closed (though some effort is going into creating common APIs at least…), and despite all the hype they are pretty underused on most actual farms.  Worse yet, most of those little startups want to get bought by the big guys… so the future of that space is one of consolidation and monopoly, like many other parts of the ag industry.

Both camps leave typical farmers scratching their heads…  either I’m irrelevant, or I need to pay many thousands of dollars per year for software I’m not sure I need?  Why is [insert big ag company’s name here] taking my data, repackaging it, and selling it back to me in the form of ‘precision ag’?  As a small to medium sized farmer, why can’t I find technology that’s useful but also affordable?  As the son of a farmer, I know that farmers are practical people – if it helps their bottom line, they’ll usually do it, but it doesn’t mean they like it.

Solution stack: “In computing, a solution stack or software stack is a set of software subsystems or components needed to create a complete platform such that no additional software is needed to support applications.” – Wikipedia

I think it’s time we invest in an open solution stack for agriculture.  Platforms and software that can deliver value to farmers today, at a reasonable cost, in a competitive ecosystem that can produce enough value for companies to succeed without fleecing farmers to pay Venture Capital firms and the bottomless stomachs of investors.  That’s not to say a healthy ecosystem of closed and open technology won’t continue to exist, but there is definitely room for alternatives.  There are good analogies here to other software stacks, like LAMP, which is still used in most websites on the internet.  LAMP allowed the web to grow faster, at less cost, with more flexibility and ultimately created more options for the end user, yet closed competitors continue to exist and fill specific market demands.

So… what functionality do we need in an open ag solution stack?

  1. Get data from sensors + the environment.  APIs to connect to existing sensors.  Access to APIs which output weather data, soil data, market information, accounting info, etc.
  2. Get data from humans.  A mobile app which can collect data from farmers, farm workers, accountants, etc. about what’s going on in the real world.
  3. View the farm and the business.  Farmers need to see maps of fields, get updates in real time of activities, get reminders about what’s next and where, etc.
  4. Get analysis and feedback.  Take inputs, run a model, generate (push) outputs.  Maybe catch a pest outbreak before it destroys your field.  Maybe pick the best time to sell your wheat.  Maybe wait a week to fertilize to avoid a rainstorm on Thursday.  That kind of thing.

These are the very basic components – like any ERP it could include so much more, or so much less depending on what the user wants.  By making a base layer of functionality available and low cost, we can move the business opportunities up the chain to services built on top of or connected to the stack.  Pay for Quickbooks to do your taxes, but use their API to integrate your financial data into your farm decision-making.  Pay Precision Hawk to do drone flyovers of your field to improve the quality of your maps, and integrate the maps into the open ag stack.  Pay an agronomist for consulting, but integrate their crop models to get real-time feedback through the open ag stack to increase accuracy and save you (and the agronomist) time.  This allows more efficient and higher quality code as many companies are invested in the same code base.

Another benefit of an open ag stack is the ability to share data.  New analytical tools can generate real value from shared data, but the current options for farms is either to forfeit their data to large companies through closed platforms (exchange data for a service model), or to keep their data completely isolated (exchange money for privacy model).  The first represents a loss of control and of value to the farmer.  The second fails to benefit from shared resources.  An open platform could allow a more flexible, middle option, where data is optionally shared fully, anonymized, or kept private, and the shared or anonymized data is accessible to all.  This would be a boon to researchers to create new methods or identify trends, and to farmers to improve decision-making.

There is some movement already towards an open solution stack. The GODAN initiative is supporting data sharing in agriculture and nutrition by improving data accessibility and collaboration between governments and NGOs.  This could lead to standardizing ag data to improve interoperability between different sensors and software.

FarmOS is a drupal based farm management platform, which emerged from the Farm Hack network.  Now used on over 200 farms across the US, FarmOS is expanding its capabilities and Michael Stenta, the main developer, is committing more time to the project.  Our-Sci is a startup which is using the software framework developed for the PhotosynQ project at Michigan State University.  Our-Sci is a research framework for data collection, sharing, and analysis.  It’s effective for creating and standardizing new methods, and developing feedback based on sensor and survey data.

But a great deal more is needed, as well as a coherent plan for development in the future.  The more organized our development, the easier for developers to contribute helpful code and the more usable a solution for farmers.  If you are interested in contributing to the creation of this open ag stack, please contact us to get involved.

 

Posted by gbathree in Blog Posts, Other Applications