AgGateway Grain Transfer Proof of Concept Shows Promise

AgGateway Grain Transfer Proof of Concept Shows Promise

AgGateway’s Grain Traceability Proof of Concept demo at Wilson Farms, Olney, IL, Oct. 26, 2017.


We talk about closed-loop systems quite a bit in modern production agriculture. Having a closed-loop, or completely self-contained, system is ideal in many instances, from downstream seed treating systems to crop protection product mixing stations.

On the precision side of things, it can be argued that we’ve yet to fully achieve a closed-loop in production data collection. There’s just a tiny piece of the puzzle missing currently.

Hear me out for a second.

We start the year with fields broken into management zones, often based on historic yield, soil type, typography, or some aggregation of myriad factors, and begin collecting as-planted data in the spring. During the summer as-applied and aerial imagery, along with weather data, are the main layers we analyze as crops poke through the soil. Then in the fall, as we remove the fiber from the fields, the all-important yield map comes into play.


However, for a true farm-to-fork tracing back of a product’s origins through the food system, there is another aspect that data collection technologies have to this point largely overlooked: grain transfer events. Or simply the movement of harvested grain from one container to another.

Recognizing that this is something that could potentially come back to bite the industry in the rear-end, especially when it comes to future traceability initiatives at the food processor level, the folks down at AgGateway have taken to trying to remedy the issue with technology.

Enter AgGateway’s Grain Traceability Proof of Concept (POC):

Using consumer-grade, Bluetooth connected data relays to various sensors across the combine, as well as deploying those same relays in each tender truck and at the scale house at the grain elevator (or in the bin if using for on-farm storage), the concept grain transfer data system logs grain transfer event data (moving grain from one container to the next – in this proof of concept the field is considered a container) that typically would be recorded by hand or, in some cases, is relayed verbally by the driver to the grain elevator operator.

“The value proposition is simply to replace paper with electronic documents, and enhance the data flow while also documenting those transfer events,” says Joe Tevis, President and Founder of VIS Consulting and an active participant in Ag Gateway precision ag initiatives. “And secondly, the data standardization part of it, or having common identifiers to streamline the flow of data.”

Tevis breaks it down further into three use cases, the first two dealing with grain transfer events on the inbound side (to the elevator) of things. The third use case is what the group refers to as “outbound.”

“That’s from the elevator to the food processor, and this is the part that is being worked on by Land O’Lakes and Purina Mills,” explains Tevis. “They’re looking at the documentation of that grain flow from the elevator to the food processor, and they too are looking to replace paper with electronic data while automating the entire (grain delivery) process as much as possible.”

Besides the physical tracking of grain unloading events, the POC spans three data standards — ISO 11783 and AgGateway’s own AgXML and ADAPT, and the process captures data across three data layers (georeferenced time stamp, source container ID and target container ID) on each unload event it “sees,” or senses.

“Really the glue is this concept known as Data Fusion, which we’re defining as the process of integrating multiple data sources with independent value propositions to create a new and greater value proposition,” Tevis says. “We’re aggregating the data to create, in this case, improved efficiency and accuracy of data transfer as the grain moves throughout the transfer process.”

Additionally, at the same time the relays are capturing and storing grain transfer data, yield monitor data is being captured to provide an estimate of total crop production in each field, while also adding to the traceability proposition. Grain cart load cell data also has a place, as it gives AgGateway members working on the POC an accurate measure of yield mass at the sub-field level, and then fleet management data on machine performance, such as combine auger status to independently verify unloading events, rounds out the aggregate.

“One of the opportunities here is knowing in real-time some of the properties of the grain we’re harvesting, then the properties of the grain as it goes into the truck, and the properties of the grain going to the elevator,” Tevis shares. “Now we can give the elevator a heads up as to, ‘Listen, we’ve got a truck coming in with this moisture content,’ or even with on-farm storage, if it’s a larger operation we can look at ‘Does this load need to go to the dryer, or can it go right to the bin?’”

POC In Action

AgGateway Grain Transfer Proof of Concept Shows Promise

Wade Wilson harvesting grain on Oct. 26, 2017, near Olney, IL.

Helping ground that vision in the dusty and dirty real-world grain harvesting environment is under the purview of Wilson Farms in Olney, IL, where AgGateway member Jeremy Wilson and his father Wade, a true pioneer of precision technology in every sense of the title, have deployed the Bluetooth sensors across its fleet of combines, tender trucks, and semis this fall as part of the POC.

“That’s consumer grade stuff,” Jeremy points out as we ride on a sunny and brisk late-October morning in a tractor pulling his Kinze grain cart, waiting for Dad to finish another load in the combine. “They use it in retail and other places, and it’s just a low wattage Bluetooth signal, a proximity sensor. It says ‘Hey look, I’m out here,’ and it just runs 24/7.”

After securing a new load of freshly harvested, Central Illinois-grown Roundup Ready corn from Dad’s combine, Wilson pulls up alongside the unload semi, and the data telematics in both machines begin to silently exchange information.

“So now this white grain truck has a relay in it as well,” he explains over the growing din of augers and engines and field computers. “Now it’s logging that ‘Hey, I’ve got the grain cart right next to me, and we’ve just got the PLM connect from CNH, we’ve got that up and running now, so now it is logging that I just engaged my PTO shaft, so we’ll be using that as another validation that says ‘Hey wait, I see another transfer event’, whether it’s from the grain cart or wherever, because now both of these trucks have it.”

Wilson recognizes that perhaps this level of grain data collection won’t be for everybody, a thought that is later confirmed when a local farmer’s curiosity gets the best of him and he drives up in his red Chevy pickup to ask Wilson just what the hell we were doing. After a quick description, the farmer laughed and asked “Why the hell you need all that?”

“You’ve got some farmers that will say ‘You know what, there’s no way I’ll ever do this’, but I look at the fact that if I fill the bin at home with non-GMO corn and I have all this data that says ‘Look, here’s all the harvest events, here’s all my transfers from this field to these trucks to this bin.’ We can do a will-call now where we don’t need to go check that bin, because here’s my planting data that says I planted ‘Hybrid XYZ’ which meets the non-GMO requirement, and here is the data set of where that grain was harvested.”

Anyone who knows Wilson knows he’s a bit of a visionary when it comes to technology (the man’s personal yield monitor collection is museum-worthy), and he guesses a commercial version of such a system might be around three to five years away. He envisions a future where semis loaded with grain pull right into the local elevator, and instantly the operator has all the production data the elevator needs on each grain load, before the driver is even done unloading.

“We’re just taking grain traceability to that next level,” Wilson says with a smile before waiving off another tracked and traced load bound for the elevator.

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