Jorge Heraud is an anomaly for a founder whose startup was recently acquired by a corporate giant: Instead of counting days to reap earn-outs, he’s sowing the company’s goodwill message.
That might have something to do with the mission. Blue River Technology, acquired by John Deere more than a year ago for $300 million, aims to reduce herbicide use in farms.
The effort has been a calling to like-minded talent in Silicon Valley who want to apply their technology know-how to more meaningful problems than the next hot app, said Heraud, who continues to serve as Blue River’s CEO.
“We’re using machine learning to make a positive impact on the world. We don’t see it as just a way of making a profit. It’s about solving problems that are worthy of solving — that attracts people to us,” he said.
Heraud and co-founder Lee Redden, who continues to serve as Blue River’s CTO, were attending Stanford University in 2011 when they decided to form the startup. Redden was pursuing graduate studies in computer vision and machine learning applied to robotics while Heraud was getting an executive MBA.
The duo’s work formed one of the early success stories of many for harnessing NVIDIA GPUs and computer vision to tackle complex industrial problems with big benefits to humanity.
“Growing food is one of the biggest and oldest industries — it doesn’t get bigger than that,” said Ryan Kottenstette, who invested in Blue River at Khosla Ventures.
Herbicide Spraying 2.0
As part of tractor giant John Deere, Blue River remains committed to herbicide reduction. The company is engaged in multiple pilots of its See & Spray smart agriculture technology.
Pulled behind tractors, its See & Spray machine is about 40 feet wide and covers 12 rows of crops. It has 30 mounted cameras to capture photos of plants every 50 milliseconds and process them through its on-board 25 Jetson AGX Xavier supercomputing modules.
As a tractor pulls at about 7 miles per hour, according to Blue River, the Jetson Xavier modules running Blue River’s image recognition algorithms need to decide whether images fed from the 30 cameras are a weed or crop plant quicker than the blink of an eye. That allows enough time for the See & Spray’s robotic sprayer — it features 200 precision sprayers — to zap each weed individually with herbicide.
“We use Jetson to run inference on our machine learning algorithms and to decide on the fly if a plant is a crop or a weed, and spray only the weeds,” Heraud said.
GPUs Fertilize AgTech
Blue River has trained its convolutional neural networks on more than a million images and its See & Spray pilot machines keep feeding new data as they get used.
Capturing as many possible varieties of weeds in different stages of growth is critical to training the neural nets, which are processed on a “server closet full of GPUs” as well as on hundreds of GPUs at AWS, said Heraud.
Using cloud GPU instances, Blue River has been able to train networks much faster. “We have been able to solve hard problems and train in minutes instead of hours. It’s pretty cool what new possibilities are coming out,” he said.
Among them, Jetson Xavier’s compact design has enabled Blue River to move away from using PCs equipped with GPUs on board tractors. John Deere has ruggedized the Jetson Xavier modules, which offer some protection from the heat and dust of farms.
Business and Environment
Herbicides are expensive. A farmer spending a quarter-million dollars a year on herbicides was able to reduce that expense by 80 percent, Heraud said.
Blue River’s See & Spray can take the place of conventional, or aerial spraying of herbicides, which blankets entire crops with chemicals, something most countries are trying to reduce.
See & Spray can reduce the world’s herbicide use by roughly 2.5 billion pounds, an 80 percent reduction, which could have huge environmental benefits.
“It’s a tremendous reduction in the amount of chemicals. I think it’s very aligned with what customers want,” said Heraud.
Image credit: Blue River
The post Goodwill Farming: Startup Harvests AI to Reduce Herbicides appeared first on The Official NVIDIA Blog.
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