A vision for better farming
In 2011, two Stanford graduate students, Jorge (the former head of precision agriculture at Trimble) and Lee (a PhD student and roboticist), met while taking Steve Blank's Lean LaunchPad course. They converged on a wild ambition: to make farming more sustainable through robotics and computer vision. Together, they built and tested their idea in the Central Valley - proving the applicability of machine learning, computer vision, and robotics to the field of agriculture. With an idea, support from friends & family, and a grant from the NSF, Blue River Technology was born.
MEET LETTUCE BOT
The first smart machine
The first smart machine - the lettuce bot - focused on lettuce thinning, a traditionally time-intensive and expensive task, The lettuce bot automates this arduous process. This first smart machine takes images, identifies which plants to remove, sprays them, and verifies the accuracy and performance of the system.
SEE & SPRAY TECHNOLOGY
The next generation technology
See & Spray is the next generation of Blue River's technology. Our next generation See & Spray machines leverage deep learning to enable our machines to identify a greater variety of plants & weeds with better accuracy, custom nozzle designs to enable 1-inch spray resolution, and improved software for faster and more agile crop protection.
Drone-based remote sensing
Evaluate plant-by-plant results of See & Spray
Blue River is also developing an unmanned aerial system that can survey a field of crops and sense for various plant and environmental characteristics. Through using a drone, this technology can enable our See & Spray technology to be more accurate - measuring and learning every step of the way.
DATA & INSIGHTS
Our Blue River machines are constantly collecting plant-specific information. Whether it's size, shape, spacing, or otherwise, we believe that we can help characterize each plant - helping farmers better understand the variables on their farm. The data and insights generated by Blue River are designed to support our vision to make every plant count.
In the news
CB Insights, Nov 2016
"Peter Thiel of Founders Fund famously commented about a perceived lack of real innovation in VC investing and startups with the following quip:
'We wanted flying cars, instead we got 140 characters.'
We'd humbly disagree with Thiel's assertion."
Blue River Technology is recognized as a game changing startup applying Computer Vision to improve the world.
Wired, May 2016
"While the developing world is hungry for agricultural knowledge, the developed world is drowning in pesticides and herbicides. In the US each year, farmers use 310 million pounds of herbicide—on just corn, soy, and cotton fields. It’s the spray-and-pray approach, not so much sniping as carpet bombing.
A company called Blue River Technology may have hit upon solution, at least as far as lettuce is concerned. Its LettuceBot looks like your typical tractor, but in fact it’s a machine-learning-powered … machine."
World Economic Forum, August 2015
"Our current methods of agriculture are not sustainable – our tools are becoming less effective, we’re damaging the ecosystems we rely on for food and we’re putting human health at risk.
Robots are the answer. [Blue River's] smart machines are able to sense each individual plant, instantly determine everything about its health, structure and needs, and precisely apply the right amount of care..."
NPR, August 2014
"... Technology is the future. For one thing, he says the ongoing labor shortage means there are not enough people to do this kind of work. So Tanimura & Antle is trying out automated lettuce thinning machines in some of its fields.
And one, developed by a Silicon Valley start-up Blue River Technology, seems to be the most promising. It’s called a LettuceBot, and it makes the decisions about which plants will go and which will stay..."
National Science Foundation
This material is based in part upon work supported by the National Science Foundation under SBIR Phase I and II grants (1143463 and 1256596). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.