>> Narrator: From the computer history museum in the heart of Silicon Valley, it's The Cube, covering Food IT: Fork to Farm. Brought to you by Western Digital. >> Hi, welcome to The Cube, I am Lisa Martin. We are at the fourth annual Food IT: Fork to Farm event in Silicon Valley at the computer history museum. An incredible event talking with ag-tech experts, technologists, and really understanding how people that the produce the food can get together with those that are innovating technology and really improve the supply chain or the food chain. My next two guests are George Kellerman, COO and general partner of Yamaha Motor Ventures and laboratory here in Silicon Valley. Welcome George! >> Welcome, thank you. >> Great to have you, and we have Nathan Dorn, COO of Food-Origins and you're also an advisor to the mixing bowl. >> Thank you Lisa. >> Absolutely. So this is a really interesting event for us. We cover a lot of tech innovation events and looking at now even the title kind of threw me when I saw Fork to Farm. We're so used to the trend of farm to table, farm to fork, and I kept reading, is that right? One of the things is that everyone's tech-enabled, right? We've got computers in our pockets. I'd love to understand Nathan from your perspective, how are you seeing the consumer, this tech-enabled food consumer really drive the food and agriculture industry which is not only contending with demanding consumers, but environmental sustainability, climate change. How is that consumer being that influential? >> They're getting vocal with their dollar, with their pocket book, and they're able to say, "I'm buying based upon values and the values just aren't cost." So they're paying up for the opportunity to know more data behind the product and contribute to the farmer. A lot of people talk about their experience at farmer's markets. It's because of their direct relationship and the feel that they have control in their engagement, that they're really becoming more empowered. The agricultural industry is taking notice and they're starting to buy into that. >> Tell us about Food-Origins, the genesis of that in context with what you just mentioned. >> So I'm a technologist in agriculture. I've been involved in agriculture since I was a child, and recently worked in a major winery and vineyard team and then later with a berry company, and realized that most of the innovations that we brought, they lacked context of economics because we just couldn't see deep enough, more granular, and measure things that mattered from people movement to where the product actually came from, the impact on whether it was quality or not, and whether the economic-- There was economic differences. We accepted that as natural variation because a farmer's job is to grow something and make it successful. If you buy a seed and you put it in the ground and you do well and make money at it, you're going to do it again. You do more of it. Their job is if I can do this well, I'll do more, rather then reinvent it. Somebody had to take on that job of reinvention and we thought Food-Origins was a big part of that. >> So from a technology perspective, if we look at the food chain from planting to evaluating soil health and fertilizer requirements, and then the post-harvest, where are you seeing the biggest opportunities for farmers to use big data analytics, connected devices, GPS devices' sensors, to glean this information, learn from these machines, to improve from we'll say farm to fork? >> The amazing thing is there's so many great companies out there that are bringing pieces of the data, whether it's soil moisture or weather, or they're imaging, flying over my fields and telling me how healthy my plants are. But the gap is in connecting that data, going from pretty pictures that are standalone or great inventions that are standalone to this is the cause and these three attributes are the effect. You know, these three attributes lead to this effect. If I can do that, if I can make that connection, we've closed the big gap. We can create that continuous learning cycle that happens automatically within a farm, We can take this art to farming, leave it as an art, but take pieces of it and make it science and allow people to connect what soil moisture does to this product that was sold weeks later. How it affected the roots, then the plant, then the fruit, and then we can make all those connections. It's in that linkage, that's where the biggest opportunities are. >> So facilitating machine leaning-- >> Yeah, absolutely. >> For the next generation farm. >> And then once you've got that machine learning, you've got the knowledge base to make those improvements, like buying the right robot for the right task, buying and having assets available at the moment they're needed, because a lot of these businesses-- Picking a berry is much different than picking a watermelon or picking an apple or a tree nut, or a piece of corn in a field. So by doing it, by having so much differences, knowing all the data ahead of time allows an innovator, a robotics company to do amazing work and make the most of their dollar asset. >> Speaking of robotics, George, Yamaha. My first thought was motorcycles. >> Absolutely. >> So tell us about Yamaha Motor Ventures. You're based here in Silicon Valley. What was the opportunity that Yamaha saw to get into the robotics space, specifically in the food and agriculture industry? >> Well when we launched Yamaha Motor Ventures two years ago, our mandate was autonomous vehicles, robotics, and industrial automation. We actually weren't looking at agriculture per se, but after meeting people like Nathan and others in the industry, it was obvious that there were opportunities for all of those, autonomous vehicles, automation, and robotics. It was just the application was a little different. Yamaha has actually a robotics division, so we have vehicles, we have robotics. Now we're looking at those platforms and technologies and looking at how we can marry them in the agricultural space. Maybe also how we can innovate new products and services. >> So in terms of adoption, what are you seeing from whether it's a generational small farm or a larger farm, where is the biggest opportunity that you see for adoption in the food chain? Is it planting, harvesting? Is it looking at drones or aerial vehicles to evaluate the health of crops? >> So I have a two part answer to that. One is people have to understand that agriculture is not just complicated. Complicated means with enough time, we could figure it out. It's complex. It's a complex system, meaning there's lots of different elements to it. We can't just assume that we can do a series of steps and it'll work, because there's going to be downstream consequences and you then have to think of those as well. It really is going to take a lot of people and a lot of different approaches, and there isn't going to be one solution or one area. You mentioned a lot of different things: drones, data collection, sensors, network connectivity, IoT. It's going to be all of those in a complex system. The system we're dealing with is complex, so the solution is also going to be complex and we have to figure out how to integrate that. It's not just enough to say here's a robot and we'll put it in the field. It's going to be well, what is the data that it's basing its decisions on and how is collecting, when? As Nathan said, knowing when to put it in the field. That's also a lot of data collection up till that point. I think actually what Nathan's focusing on is we have to start with data. We need to build that historical data where we can apply machine learning to it. We have to start somewhere, and that data is going to come from drones, from sensors, from a lot of different networks. It might just be putting sensors on the vehicles that are in the field now. >> Right. But they're connecting different kinds of data, not just GPS, but they might be collecting hyper spectral imagery to detect disease and insect infestation, the health, the vitality of the plants and the fruit. So there's a lot of opportunities, but this is not a five year solution. This is a generational, multi-generational solution that we have to come up with. >> And is it also a multi-educational step process with farms across the US to really understand how to maybe deconstruct this complexity so they can understand the value that can be gleaned? >> A lot of the farmers I talk with, they'll tell me point blank, they're not farmers. They're people who foster and help the biological system of plants growing and creating produce. They're there to facilitate that. They're not there to do that, but think about innovation as a whole. A farmer has a super multi-skilled, multi-disciplinary skill set. Whatever innovations we bring have to fit in an entire skill set of a farmer, whether it's human resources manager, chemist, biological expert, soil scientist, mechanic. It has to fit an economist. They have to be able to match all those things, so it's going to take people that want to be engaged and have a passion for changing that system and being involved in that system to help carry it to that next step I think. It's going to take people like Yamaha Ventures. >> Well I think fortunate for them that they have people like you who are leading them on the way. George and Nathan, we want to thank you so much for sharing your insights on The Cube with us today. We wish you the best of luck in ventures. >> Thank you. >> Yeah, appreciate it. >> We want to thank you for watching The Cube. Again, we are at the Food IT: Fork to Farm event at the computer history museum in Silicon Valley. I am Lisa Martin, stick around. We have great guests coming up next. (electronic music)