Why Horticulture Is Ripe for Artificial Intelligence

LUNA-iUNU

Editor’s Note: Laura Drotleff, former Editor of Greenhouse Grower, wrote this article before she left Meister Media to pursue another job opportunity.

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Artificial intelligence (AI) is everywhere in the news right now, from autocorrecting cell phones to self-driving cars to new forays into agriculture by large companies like Microsoft. In many ways, it is an overarching term for a number of different tools that it encompasses, from computer vision to machine learning to deep learning to actual robots. It’s more complex than one broad definition, which experts worldwide are still struggling to come up with and agree upon. Add to that the other technologies that interface with AI — like the Internet of Things (IoT), big data, and Blockchain, among others — and it gets even more complex.

Machine learning is often described as a subdiscipline of artificial intelligence. It includes current state-of-the-art technology that shows the most promise at providing the tools both industries and society can use to drive change. Conversely, deep learning is work aimed at genuinely simulating human reasoning. While machine learning takes the core ideas of AI and focuses them on solving real-world problems with neural networks designed to mimic our decision making, deep learning focuses even more narrowly on a subset of machine learning tools and techniques and applies them to solving just about any issue.

Many Moving Parts to AI

Mainstream manufacturers have been incorporating AI into their processes for decades. Boeing started in the 1980s by photographing each part or widget before assembly, according to Matt King, Chief Technology Officer at iUNU, which launched the LUNA AI platform for plant health in August 2017. King worked for Boeing for several years and employed the manufacturer’s use of AI for its processes there as a model for the development of the LUNA system.

The iUNU team is led by Adam Greenberg, who grew up in horticulture in San Francisco as the son of a botanist. A University of Washington graduate, Greenberg co-founded a clean water startup in 2013, and also worked at Amazon from 2011 to 2013.

Because plant manufacturing is more complex than building airplanes, King says, it’s not surprising that it has taken manufacturing automation technology until now to reach the point of offering the same advantages to the horticulture industry.

It’s only been within the last few years that AI has made its debut in the agriculture sector. We’re now seeing a rapid rollout of the technology thanks to unprecedented investment in this area. In the first half of 2017, novel farming systems raised $198 million across 21 deals, a 560% increase year over year, according to AgFunder.

Similar to the adoption curve of smartphones, eventually most greenhouse growers will be able to afford to use AI in their operations, according to Ryan Hooks, CEO of Huxley, an Amsterdam-based company that has rolled out its own new artificial intelligence platform, operated through augmented reality. When technology starts as a new and novel concept, naturally the cost will be higher, but as more growers implement AI, it’s likely to scale down in price.

So Does This Mean Robots Will Replace Greenhouse Labor or Growers?

With rapidly advancing technology and the astounding ongoing development of machine learning and deep learning, it’s possible that one day our own cerebral capabilities could be surpassed by artificial intelligence. But for now, it’s not likely that humans or human jobs will be completely displaced. Tech companies say AI will create more jobs than it destroys, but that human skills must shift to accommodate technology.

In regard to jobs being replaced by AI, the opinions on the role of AI in industrial fields like agriculture are as broad and varied as the definition of AI itself.

On one hand, there are those who think there will be significant changes to both repetitive and skilled employment coming in the near future. These tech aficionados are predicting that even high-level thinking jobs like attorneys, financial analysts, chefs, and journalists could be replaced by intelligent robots. Then there are the skilled labor positions that are harder to find these days, like truck drivers (think driverless trucks), manufacturing workers, bricklayers, and other manual labor positions that are ripe for AI replacement.

In the near term, Huxley is one company working to create a blend of augmented reality and artificial intelligence that could, in a sense, robotize any human and turn him or her into a grower by providing information at a glance and access to guidance from experts at the click of a button.

Fear It or Embrace It

With this basic knowledge of artificial intelligence, hopefully any fear you may have harbored about the potential for applying it to your operation has been put at bay. As Lili Cheng, Microsoft’s Corporate Vice President of AI and Research, wrote in Time magazine in 2017, we have had to adapt to technology for decades now, but AI is turning that dynamic around by creating technologies that adapt to us instead.

Think of it as an opportunity to replace smaller tasks in much the same way that certain innovations in automation equipment already have simplified repetitive tasks, allowing growers to move labor resources to more valuable functions. What’s beneficial about AI is that in addition to reducing labor, it can also learn as it works, creating a bank of knowledge that growers can use to promote continuous improvement.

However, not all AI technologies are created equal. Machine learning, for example, has become so mainstream and off-the-shelf in many cases that any company could claim it has an AI platform that will work for you. The key is in determining which companies are credible and which are not.

If you consider adopting AI technologies in your operations, ask specific questions about what the technology does, how your data will be stored, who retains ownership of your data, what the return on investment will be, and how it can enhance your operation’s efficiency.