Securing the Future of Sustainable Food

Why are the stakes so high now?

If we don’t produce food more efficiently and sustainably, ensuring food security will push the earth’s resources beyond its limits. Growing crops will become more difficult, biodiversity and soil health will degrade, and water supplies will become more and more limited. Our food systems urgently need to become more productive while impacting the planet less.

The stakes couldn’t be higher, but we’re optimists. And we’re working relentlessly to help every farmer, researcher, crop breeder, and advisor overcome the challenges they face and build a more productive and resilient food system. Mineral’s perception technology is a key piece of the puzzle to help make the changes that the industry and our ecosystem demand.

What types of transformation can we expect to see with AI and perception technologies applied to agribusinesses?

Grocery retailers and growers should expect system-wide changes enabled by AI. For example, AI is well suited to making better forecasts of crop yield, thanks to its ability to integrate huge datasets. Higher confidence in the future and better visibility to on-farm conditions with perception means less waste and volatility in the supply chain and will unlock a burst of innovation in risk management products like insurance and financing.

We’re also seeing the early indication of AI empowering agronomists with more localized, context-aware advice. This could help smaller farms remain competitive and encourage more specialization in crops or practices, and the adoption of complex practices like regenerative agriculture.

Third, many tedious tasks across agriculture can be augmented by AI, freeing up human experts to do higher-value work. For example, most crop breeders would prefer to run crop trials in locations that closely match the target environment for the variety. But that often isn’t possible as trained breeders are scarce and collecting data is labor-, time- and skill-intensive. With AI’s integration into phenotyping, running trials in 10x or 10,000x more locations becomes conceivable.

And finally, AI will add to the value provided by equipment and input companies, retailers, service providers, cooperatives, and farmers. Farmers will be able to efficiently farm more acres; more sophisticated

What does perception technology for agriculture mean?

AI’s ability to capture and understand precise information from plants through computer vision will become increasingly valuable for growers, and foundational to the future of sustainable agriculture. It’s also critical that this functionality is specially designed to operate within the complexities of the food production system.

Together with our partners, Mineral is developing AI tailored specifically to agriculture to help:

• Improve profitability by increasing the precision, efficiency, and effectiveness of agriculture practices and robotics across geographies and crop types, like finding ways to more precisely target yield-limiting weeds;
• Accelerate crop trials by applying perception technology to field phenotyping and AI to the implementation of knowledge that comes from modeling partner data sets, driving a new way of thinking; and
• Forecast and understand the drivers of yield more precisely, which will be foundational to planning and preparation for feeding the planet’s population under more extreme growing conditions, such as drought and high temperatures.

What benefits will more AI-powered tech solutions bring to the grocery industry?

Let’s take quality control as an example. Fresh produce inspections are primarily used to simply accept or reject an item that doesn’t meet a standard. And it’s been this way for 50 years or more. If AI were integrated into the process of checking produce for characteristics like bruising, disease, and mold, human inspectors could collect larger volumes of data more consistently, helping “quality control” evolve into “quality management.” With new insights unlocked up and down the supply chain, each data point is an opportunity to close the feedback loop with growers to help them optimize their crop management and minimize food waste and operational inefficiencies, resulting in improved quality and shelf life in future production cycles.

How can suppliers to the grocery industry work with Mineral?

Mineral develops foundational high-performance machine-learning models to help suppliers do everything from predicting crop yields and targeting pests and weeds to minimizing chemical use.

We offer software and hardware tools that make sense of diverse sources of information that were previously too complex or overwhelming to be useful.

Case Study: Mineral Teams Up with Driscoll’s for Greater Efficiencies

Mineral’s agriculture-specific AI was put to work in partnership with Driscoll’s.

“We were really able to see our technology working in action through our partnership with Driscoll’s,” said Mineral’s chief executive officer Elliott Grant.

Initially, Mineral worked with Driscoll’s to use robotics and AI to measure traits — such as the length of strawberry stems or the size of individual raspberries — on thousands of plants in trial plots. Driscoll’s breeders were able to collect data at scale continuously through the season, freeing them up to focus on other tasks.

“We also worked with the Driscoll’s team to improve yield forecasts. Integrating hundreds of different data types, including time-series data, is well-suited to the power of AI,” Grant said.

By working hand-in-hand with Mineral’s engineers and data scientists, Driscoll’s significantly improved its forecast accuracy and increased the frequency of forecasting, and can extend its forecasting horizon.

“One of the more exciting discoveries to me is that the AI models can create a deeper understanding of the drivers of berry yield, helping develop new insights for Driscolls’ experts.”