Agricultural Tech

Computer vision is unlocking new avenues in agricultural technology. Smart cameras and algorithms allow for large-scale crop monitoring without having to manually look at each acre. We can help.

Our platform powers:

Training data for computer vision

We’ve helped some of the most innovative companies in the agriculture space with their vision models. As cameras and crop surveillance are becoming more commonplace, machine learning solutions can actually teach those cameras to see, allowing you to monitor thousands of acres automatically.

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Test your models

We don’t just train cutting-edge computer vision models, we test them too. We’re experts on human-in-the-loop machine learning and understand how to combine the best of human and machine intelligence to fine-tune your models, making them more accurate, more confident, and more valuable.

Tune your models

The last component of active training is model tuning. We’ll look at your algorithm’s output and either validate when it’s right or correct it when it isn’t. Those judgments can then be fed back into your model to improve its output. This loop is both what Figure Eight was founded on and how machine learning works best in the real world.

If you’re looking to use machine learning in the ag tech space–no matter where you are in the process–we can help. Even if you’re just getting started, we’ll scope your project and make sure you get exactly what you need from your training data.

Trusted by today’s leading brands

We needed a way to scalably measure search results quality. We needed to look at the impact of algorithms in search results relevancy and Figure Eight provides a way to do that. Our search results have gotten measurably better and we’ve been able to increase revenue, so it's been a real winner in terms of user experience and income.

James Rubenstein

Director of U.S. Search

Having tens of thousands of people at your disposal to read content and score it for sentiment and tone gives us the ability to inform our clients if there’s an issue. We can identify it quickly and put the campfire out before it becomes a forest fire.

Chris Lightner

Executive Vice President, Measurement and Insights

Machines don’t know music. Machines don’t feel the beat. Machines don’t feel happy. So we have to teach them. Teaching them involves two things: what do you put in, but also how do you grade your machines?

Henriette Cramer

Senior Research Lead



What We Learned Labeling 1 Million Images eBook

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7 Advances Pushing the Boundaries of Computer Vision

Before we can think critically about computer vision, we need to take a moment to appreciate our own human vision...

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A whirlwind tour of image processing use cases

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