Disrupted Food – how AI and IoT will solve the world food shortages

AI, IoT, 3D printing – positively changing food production globally

iDisrupted Commentary

We’ve covered a lot about positive disruption in food over the past year – from IoT in farming to autonomous farms to 3D printed food.
It all boils down to efficiency – the way we use and nurture food production and with real time analytics from IoT in farming a significant reduction in food wastage ($1 trillion a year in wasted food). . . this article from Wired adds some skin on the bone (sorry):
“Humanity’s got itself a problem. As Homo sapiens balloons as a species—to perhaps nearly 10 billion by 2050—the planet stubbornly stays the same size, meaning the same amount of land must support way, way more people. Add the volatility of global warming and consequent water shortages, and the human race is going to have some serious trouble feeding itself.

Perhaps it’s serendipitous, then, that the machines have finally arrived. Truly smart, truly impressive robots and machine learning algorithms that may help usher in a new Green Revolution to keep humans fed on an increasingly mercurial planet. Think satellites that automatically detect drought patterns, tractors that eyeball plants and kill the sick ones, and an AI-powered smartphone app that can tell a farmer what disease has crippled their crop.

Forget scarecrows. The future of agriculture is in the hands of the machines.

Deep learning is a powerful method of computing in which programmers don’t explicitly tell a computer what to do, but instead train it to recognize certain patterns. You could feed a computer photos of diseased and healthy plant leaves, labeled as such. From these it will learn what diseased and healthy leaves look like, and determine the health of new leaves on its own.

That’s exactly what biologist David Hughes and epidemiologist Marcel Salathé did with 14 crops infected by 26 diseases. They fed a computer more than 50,000 images, and by learning on its own, the program can correctly identify 99.35 percent of the new images they throw at it.

Still, those are manipulated images, with uniform lighting and backgrounds so it’s easier for the computer to make sense of the leaves. Pluck an image of a diseased plant from the Internet and feed it to the computer and the accuracy is around 30 to 40 percent.

Not terrible, but Hughes and Salathé hope to see this AI power their app, PlantVillage, which currently allows farmers around the world to upload a photo of their ailing plants to a forum for experts to diagnose. To smarten up the AI, they’ll continue feeding it photos of diseased plants. “More and more images from various sources, in terms of how the pictures were taken, time of year, location, and so on,” says Salathé. “And the algorithm can just pick up on that and learn.”

This isn’t simply a matter of ferreting out infections: Plenty of other things beat plants up. “Most diseases that hamper growers are physiological stresses, so not enough calcium or magnesium or too much salt or too much heat,” says Hughes. “People often think it’s a bacterial or fungal disease.” Misdiagnoses can lead to farmers wasting money and time on pesticides or herbicides. In the future, AI could help farmers quickly and accurately pinpoint the problem.

After that, the humans will wrest back control—because while an app might be able to find the problem, only an extension expert can tailor a solution to a specific climate or soil or time of year. The UN’s Food and Agriculture Organization considers such technology a “useful tool” for crop management, but the expert’s word is doctrine. Thus, says Fazil Dusunceli, a plant pathologist with the FAO, such electronic results are welcome, but “final pest management decisions should be taken in collaboration with experts on the ground.”

Tractor Trainer

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.

 

Blue River claims the LettuceBot can roll through a field photographing 5,000 young plants a minute, using algorithms and machine vision to identify each sprout as lettuce or a weed. If that seems too impossibly fast to you, “it’s well within the computing of machine learning and computer vision,” says Jeremy Howard, founder of deep-learning outfit Enlitic. A graphics chip can identify an image in just .02 seconds, he adds.

With an accuracy within a quarter inch, the bot pinpoints and sprays each weed on the fly. If it eyeballs a lettuce plant and determines it isn’t growing optimally, it’ll spray that too (farmers overplant lettuce by a factor of five, so they can sacrifice plenty of extras). If two sprouts ended up too close to one another during planting (not ideal), the machine can discern them from, say, one particularly large plant, and zap them as well…more