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Published on June 15th, 2021 | by Emergent Enterprise

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John Deere Turned Tractors into Computers — What’s Next?

Emergent Insight:
There is a powerful computer in our pocket. There’s another one parked in our garage. We have a conversation with yet another in the kitchen. What were once emergent technologies are now embedded into our everyday lives. That tractor in the field? It’s now a “mobile sensor suite” according to the CTO at John Deere in this interview at The Verge by Nilay Patel. Agricultural machinery is now an incredible combination of technological and mechanical engineering using internet of things, artificial intelligence, 5G, satellite imagery, mobile apps and more. Farmers are now not only harvesting crops but also important critical data to be more profitable and efficient.

Original Article:
Photo: John Deere

CTO Jahmy Hindman on farming, data, and right to repair

One of our themes on Decoder is that basically everything is a computer now, and farming equipment like tractors and combines are no different. My guest this week is Jahmy Hindman, chief technology officer at John Deere, the world’s biggest manufacturer of farming machinery. And I think our conversation will surprise you.

Jahmy told me that John Deere employs more software engineers than mechanical engineers now, which completely surprised me. But the entire business of farming is moving toward something called precision agriculture, which means farmers are closely tracking where seeds are planted, how well they’re growing, what those plants need, and how much they yield.

The idea, Jahmy says, is to have each plant on a massive commercial farm tended with individual care — a process which requires collecting and analyzing a massive amount of data. If you get it right, precision agriculture means farmers can be way more efficient — they can get better crop yields with less work and lower costs.

But as Decoder listeners know by now, turning everything into computers means everything has computer problems now. Like all that farming data: who owns it? Where is it processed? How do you get it off the tractors without reliable broadband networks? What format is it in? If you want to use your John Deere tractor with another farming analysis vendor, how easy is that? Is it easy enough?

And then there are the tractors themselves — unlike phones, or laptops, or even cars, tractors get used for decades. How should they get upgraded? How can they be kept secure? And most importantly, who gets to fix them when they break?

John Deere is one of the companies at the center of a nationwide reckoning over the right to repair. Right now, tech companies like Samsung and Apple and John Deere all get to determine who can repair their products and what official parts are available.

And because these things are all computers, these manufacturers can also control the software to lock out parts from other suppliers. But it’s a huge deal in the context of farming equipment, which is still extremely mechanical, often located far away from service providers and not so easy to move, and which farmers have been repairing themselves for decades. In fact, right now the prices of older, pre-computerized tractors are skyrocketing because they’re easier to repair.

Half of the states in the country are now considering right to repair laws that would require manufacturers to disable software locks and provide parts to repair shops, and a lot of it is being driven — in a bipartisan way — by the needs of farmers.

John Deere is famously a tractor company. You make a lot of equipment for farmers, for construction sites, that sort of thing. Give me the short version of what the chief technology officer at John Deere does.

[As] chief technology officer, my role is really to try to set the strategic direction from a technology perspective for the company, across both our agricultural products as well as our construction, forestry, and road-building products. It’s a cool job. I get to look out five, 10, 15, 20 years into the future and try to make sure that we’re putting into place the pieces that we need in order to have the technology solutions that are going to be important for our customers in the future.

One of the reasons I am very excited to have you on Decoder is there are a lot of computer solutions in your products. There’s hardware, software, services that I think of as sort of traditional computer company problems. Do you also oversee the portfolio of technologies that [also] make combines more efficient and tractor wheels move faster?

We’ve got a centrally-organized technology stack organization. We call it the intelligent solutions group, and its job is really to do exactly that. It’s to make sure that we’re developing technologies that can scale across the complete organization, across those combines you referenced, and the tractors and the sprayers, and the construction products, and deploy that technology as quickly as possible.

One of the things The Verge wrestles with almost every day is the question of, “What is a computer?” We wrestle with it in very small and obvious ways — we argue about whether the iPad or an Xbox is a computer. Then you can zoom all the way out: we had Jim Farley, who’s the CEO of Ford, on Decoder a couple of weeks ago, and he and I talked about how Ford’s cars are effectively rolling computers now.

Is that how you see a tractor or a combine or construction equipment — that these are gigantic computers that have big mechanical functions as well?

They absolutely are. That’s what they’ve become over time. I would call them mobile sensor suites that have computational capability, not only on-board, but to your point, off-board as well. They are continuously streaming data from whatever it is — let’s say the tractor and the planter — to the cloud. We’re doing computational work on that data in the cloud, and then serving that information, those insights, up to farmers, either on their desktop computer or on a mobile handheld device or something like that.

As much as they are doing productive work in the field, planting as an example, they are also data acquisition and computational devices.

How much of that is in-house at John Deere? How big is the team that is building your mobile apps? Is that something you outsource? Is that something you develop internally? How have you structured the company to enable this kind of work?

We do a significant amount of that work internally. It might surprise you, we have more software development engineers today within Deere than we have mechanical design engineers. That’s kind of mind-blowing for a company that’s 184 years old and has been steeped in mechanical product development, but that’s the case. We do nearly all of our own internal app development inside the four walls of Deere.

That said, our data application for customers in the ag space, for example, is the Operations Center. We do utilize third parties. There’s roughly 184 companies that have been connected to Operations Center through encrypted APIs, that are writing applications against that data for the benefit of the customers, the farmers that want to use those applications within their business.

One of the reasons we’re always debating what a computer is and isn’t is that once you describe something as a computer, you inherit a bunch of expectations about how computers work. You inherit a bunch of problems about how computers work and don’t work. You inherit a bunch of control; API access is a way of exercising control over an ecosystem or an economy.

Have you shifted the way that John Deere thinks about its products? As new abilities are created because you have computerized so much of a tractor, you also increase your responsibility, because you have a bunch more control.

There’s no doubt. We’re having to think about things like security of data, as an example, that previously, 30 years ago, was not necessarily a topic of conversation. We didn’t have competency in it. We’ve had to become competent in areas like that because of exactly the point you’re making, that the product has become more computer-like than conventional tractor-like over time.

That leads to huge questions. You mentioned security. Looking at some of your recent numbers, you have a very big business in China. Thirty years ago, you would export a tractor to China and that’s the end of that conversation. Now, there’s a huge conversation about cybersecurity, data sharing with companies in China, down the line, a set of very complicated issues for a tractor company that 30 years ago wouldn’t have any of those problems. How do you balance all those out?

It’s a different set of problems for sure, and more complicated for geopolitical reasons in the case of China, as you mentioned. Let’s take security as an example. We have gone through the change that many technology companies have had to go through in the space of security, where it’s no longer bolted on at the end, it’s built in from the ground up. So it’s the security-by-design approach. We’ve got folks embedded in development organizations across the company that do nothing every day, other than get up and think about how to make the product more secure, make the datasets more secure, make sure that the data is being used for its intended purposes and only those.

That’s a new skill. That’s a skill that we didn’t have in the organization 20 years ago that we’ve had to create and hire the necessary talent in order to develop that skill set within the company at the scale that we need to develop it at.

Go through a very basic farming season with a John Deere combine and tractor. The farmer wakes up, they say, “Okay, I’ve got a field. I’ve got to plant some seeds. We’ve got to tend to them. Eventually, we’ve got to harvest some plants.” What are the points at which data is collected, what are the points at which it’s useful, and where does the feedback loop come in?

I’m going to spin it a little bit and not start with planting.

I’m going to tell you that the next season for a farmer actually starts at harvest of the previous season, and that’s where the data thread for the next season actually starts. It starts when that combine is in the field harvesting whatever it is, corn, soybeans, cotton, whatever. And the farmer is creating, while they’re running the combine through the field, a dataset that we call a yield map. It is geospatially referenced. These combines are running through the field on satellite guidance. We know where they’re at at any point in time, latitude, longitude, and we know how much they’re harvesting at that point in time.

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The Emergent Enterprise (EE) website brings together current and important news in enterprise mobility and the latest in innovative technologies in the business world. The articles are hand selected by Emergent Enterprise and not the result of automated electronic aggregating. The site is designed to be a one-stop shop for anyone who has an ongoing interest in how technology is changing how the world does business and how it affects the workforce from the shop floor to the top floor. EE encourages visitor contributions and participation through comments, social media activity and ratings.



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