Alphabet X graduates robotic agtech company Mineral
A little over two years after its public debut, Mineral is becoming its own Alphabet company. The team formerly known as the“ Computational Agriculture Project” just completed the X“ Moonshot” labs. “After five years of incubating our technology at X, Alphabet’ s moonshot factory, Mineral is now an Alphabet company,” CEO Elliott Grant said in a blog post.“ Our mission is to…
A little over two years after its public debut, Mineral is becoming its own Alphabet company. The team formerly known as the “Computational Agriculture Project” (no awards to guess why they took on the new name) just completed the X “Moonshot” labs.
“After five years of incubating our technology at X, Alphabet’s moonshot factory, Mineral is now an Alphabet company,” CEO Elliott Grant said in a blog post. “Our mission is to scale sustainable agriculture. We do this by developing a platform and tools that help collect, organize and understand information about the plant world never before known or understood – and make it useful and actionable.”
Years after attempting to build a robotics division largely through acquisitions, Alphabet appears to be building another organically in-house. Mineral follows Everyday Robots and Intrinsic as X grows into a fully shared subsidiary of Alphabet.
Mineral uses its in-house robots to create datasets and conduct research on various crops. It explains that – over the half-decade of its (mostly clandestine) existence – it has been found that most companies are doing a good job of collecting the scale of data needed to leverage machine learning.
“There is no single mode of data collection that is right for every farming task or crop,” says Grant. “We started with a plant rover capable of capturing massive amounts of high-quality imagery and expanded over time to building general-purpose sensing technology that can work across platforms such as robots, third-party farming equipment, drones, Sentinel devices and cell phones .”
The company’s ultimate goal is to create detailed and rich datasets that can be used by farmers around the world to unlock previously unknown factors in cultivation. In doing so, she wants to contribute to the cultivation of plants that are more resilient to climate change without exacerbating the urgent problem.
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