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

Walking through the field, taking tallies on how many weeds we hit, how many we missed, and our accuracy was in the high 90s on that test. We’re really excited about bringing this technology to the farmer and using AI to really modernize farming.

Chris Padwick

Machine Learning Team Lead

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Learn how Home Depot uses machine learning and high quality data to make every customer experience, no matter how they’re shopping, a whole lot better.
When we changed to Figure Eight, within a few weeks, we saw that labeler accuracy go up to 88% and it stayed in the high 80s and 90s for us ever since, even across a large diversity of models. That’s been a really, really big win.

Etienne Manderscheid

VP Engineering, AI - Machine Learning

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Resources

FeaturedeBooks

eBook: What We Learned Labeling 1 Million Images

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eBooks

The Essential Guide to Training Data

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“Getting Started with Figure Eight” Monthly Webinar

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