Mineral CEO sees agtech company as tool for growers to improve productivity

Mineral CEO Elliott Grant discusses the agtech company’s vision for serving the agriculture community.
Mineral CEO Elliott Grant discusses the agtech company’s vision for serving the agriculture community.
(Photo: Rawpixel.com, Adobe Stock)

The Packer’s Tom Karst recently spoke with Elliott Grant, CEO of Mineral, about the agtech company’s vision for serving the agriculture community.

California-based technology giant Alphabet unveiled Mineral in January 2023 as a company with the ambition to help scale sustainable agriculture. Mineral was spun off from X, the “moonshot factory” R&D facility of Alphabet, which is also the parent company of Google.

From this Q&A:

  • How the idea for Mineral developed.
  • Which crops can benefit from this technology.
  • How farmers and growers will engage with Mineral.
  • Companies involved in the trial stage.
  • Mineral's relationship to the entire supply chain.
  • How ownership of data will be handled.

Related: Alphabet raises curtain on agtech firm


Tom Karst, The Packer: Elliott, thanks for taking time to talk about Mineral. As you look back on how the idea (for the company) was born, what are some things that you recall in terms of the progression of this concept?
 
Elliott Grant: Thanks, Tom. Let me start by going back to before I joined X. I've spent 17 years in the ag and food industry working to bring breakthrough technologies to scale, including working on traceability solutions for the produce supply chain. During that time, I saw the opportunity for better data and analytics to transform business operations and have a huge impact on a critical industry. 

Mineral CEO Elliot Grant
Mineral CEO Elliott Grant (Photo courtesy of Mineral)

Six years ago, I joined X with the mission to help improve the sustainability of global agriculture — not just produce, but the entire food industry. I approached that problem with the insight that despite being a decision-intensive business, it's very challenging to collect data in agriculture. 

How to reconcile these two things? Every grower knows they are constantly having to make decisions under a lot of uncertainty — and sustainable farming adds even more complexity. So, we started Mineral with this idea: If we're going to help solve for sustainability, we have to address the data challenge. And we have to provide users — farmers, agronomists, advisors, researchers — better tools to help them make decisions under uncertainty.
 
As the industry inexorably moves towards greater sustainability, we're having to address things like how to be more precise, how to use fewer inputs, how to reduce waste; the complexity has also increased — and therefore the need for better data and analytics has increased. It's a challenging time for the industry, for sure, but the good news is that AI (artificial intelligence) technology has reached the maturity that I think we can now bring [these] tools to our partners.
 
I would imagine maybe some of the tools involve satellites and other machines that would survey farmland. If you would, describe the set of tools that Mineral will have to help gather the data and process that data for the growers.
 
The farmer today can access many of types of data. Satellite data as you mention, soil data, weather data; farm equipment is increasingly generating data, and growers collect management practice data, irrigation data, picker data, yield and quality data.

Visualize hundreds of layers of data, some of which are new and coming from new sensors [on the farm], some of which already exist. The challenge has been collecting enough of the right kinds of data and making sense of it all.

One of the core capabilities we have developed is the ability to collect, organize and analyze a wide variety of different data types and do it at the scale and accuracy that farmers need. 

I will use an example to illustrate. Very often, the produce industry wants to know what yield will be two weeks from now. We are bringing the power of machine learning (ML) to that question. It is very difficult to do [yield estimation] accurately with traditional tools because it’s so multidimensional, but ML is ideally suited to those kinds of problems. Unlike traditional statistics, the most advanced ML tools can now handle different types of data — and huge quantities of data — and discover subtle patterns that are not obvious to humans. 

But we also learned something surprising. Not only could AI and ML improve yield forecasting, but they help human experts improve their understanding of the drivers of yield.

Human experts have intuition the algorithm doesn't have. So, this ability for human experts and ML to work together is what we're seeing emerge, and that’s a really exciting development for the industry.
 
Part of our job is to work closely with our partners to [determine] what's the right type of information we need to collect to put into their model; it may be satellite data or historical data or management practice data. In other situations, it might be that they need to get plant image data using a rover or a drone or a phone from a field.
 
There is no single answer to what is required. 
 
The big takeaway is we, as an industry, can now analyze more data, and more types of data, than ever before. The other thing I tell our partners is to think about collecting data now — even if you don’t immediately have the ability to use it. Because it’s impossible to go back in time and collect that historical data, and these AI models are very data hungry.
 
When you're thinking about the crops that could use this technology or toolkit, is it a particular set of crops? It could be anything, right?
 
We were very intentional when we started Mineral to not focus on only one type of crop. Often agtech gravitates toward row crops — such as corn and soybeans — because that’s where the acreage is. I have a real soft spot for specialty crops because of the nutrition value proposition, the connection to consumers, the challenge of perishability, and so on. And there are other crops such as common bean and cassava which are often underserved by the tech industry and commercial industry but are an incredibly important source of food security for hundreds of millions of people.

We actually see it as a strength that we apply the same technologies in very different domains. This is one of the unexpected benefits we've seen with machine learning that it gets better the more diversity it learns. 
 
How will farmers and growers engage with Mineral?
 
Farmers have very strong relationships with their suppliers and advisers that have developed over years or even generations. Our job is to provide those trusted partners better tools and capabilities. I think the opportunity is to help them be even more effective agents for their customers. Our mission is to work with these critical intermediary layers and give them the tools to help them do that.
 
Who are the customers or companies that have been working with you in the trial stages? Can you talk about those?

Since 2017, we’ve partnered with companies across the supply chain — from global leaders in crop protection and seed breeding to leading produce companies to research institutes and universities — to work hand in hand and reimagine food production. 

For example, we equipped the team at Driscoll’s with perception technology and AI models that help them create new knowledge about the drivers of berry yield and quality. We’ve also partnered with leading crop protection company Syngenta to enable farmers to deploy their products with AI- and ML-powered precision. 
 
With your previous work on traceability, you worked with retailers and growers. How do you see Mineral being relevant to the whole supply chain?
 
The food system is tightly interconnected. Our mission is to serve the whole supply chain, not just solve one piece of the puzzle. Historically, not just the produce industry, but the food industry in general has become very siloed, in which you end up optimizing for a narrow business problem, because it's difficult to optimize holistically. 

As we move towards a future of greater sustainability and less waste, we will need to improve the way the supply chain interacts. And this is not a new idea. To answer your question, we see this technology and our offerings bringing value to the breeders, the input providers, the farmers and their agronomists, the shippers, processors and retailers. We can become part of that connected layer that helps the industry, not optimize for a single link, but optimize for the whole chain.
 
How will ownership of the data be managed?
 
We come out of a business that has earned the right to be a brand trusted by consumers and enterprises. As a result, we understand security, we understand privacy — and the technical hard work that is needed to ensure those are delivered reliably. We take data security and ownership extremely seriously.

As a principle, grower data belongs to the grower. That grower can choose to share the data with a trusted partner, such as their agronomist or data processor. There can be a great deal of benefit in helping that grower compare their data to peers — but while maintaining their privacy. We have tools to help users get that value from a network without losing control of ownership or anonymity.  

How do you see Mineral evolving in the next five years?
 
I'm very energized by the potential we have to help this industry achieve what it's trying to achieve to increase productivity, to reduce inputs (such as water), to reduce waste, to improve decision-making and to connect the supply chain. I believe AI and ML will enable digital transformation of the industry and I look forward to partnering with like-minded forward-thinkers to drive real outcomes.

 

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