January 8, 2019 By Dr. Jason Behrmann
Rapid innovation in machinery and computer technology have lowered barriers to entry within automation. New cutting-edge tools, from robots to artificial intelligence (AI), capable of executing complex tasks are increasingly available—and affordable. Of particular significance is that fundamental components of automation technologies are now easier to customize.
Being affordable and more customizable, these developments enable specific automation tools for individual businesses, including the family farm. Greenhouse farming is no exception; however, discussions about greenhouse automation tend to focus on robots and machinery, leaving little mention of stealth technologies that do important work behind the scenes. It’s time to bring these technologies into the limelight.
Artificial intelligence: often unseen but important nonetheless
Talk of a future where robots replace greenhouse workers is arguably premature and overlooks important categories of greenhouse automation technologies on the market today. One noteworthy development is AI tools that serve as digital assistants, not replacements, for greenhouse workers. Contrary to popular thought, AI innovations are often unseen computer analyses that have no connections to robots.
Technology companies can now train sophisticated smart software about tasks in greenhouse farming using “big data”–being lots of digital information about daily greenhouse operations. Big data can be anything from records of your power consumption to digital photos of your crops. From these troves of digital information, we can train AI software to learn many common, repetitive tasks in greenhouse farming (see here for a simple video tutorial on AI-automation and big data for greenhouse farming). Once the smart software understands these tasks, it can reinforce the decision-making capacities completed by highly-skilled employees today. Rather than replace, these AI-automation tools aim to empower these skilled employees with a means to streamline their daily operations.
Consider the following examples. The first pertains to crop pest and disease diagnosis by AI. Using vast data banks of images of diseased and infested crops, smart software learned patterns in spots, scars and insects on plants. With the help of a smartphone app, you can now take a photo of a diseased crop in your greenhouse and obtain a diagnosis of the problem, along with a recommended treatment strategy. The power for this technology to streamline and automate critical tasks in crop disease management are obvious. Best of all, you can hold the technology in your hand since all the complex computations that automate crop disease diagnoses occur on an unseen computer server far away.
Additional examples arise from big data from your growing conditions. Using records of your heating, lighting, humidity, and more, AI software can understand the multiple factors that influence the growth of your greenhouse plants. Once known, the smart software can analyze your current growing conditions and make accurate predictions about the future productivity of your greenhouse. Predicting factors such harvest yields and flowering time for ornamentals are now possible and provide means to reduce the time you devote to manual, repetitive plant assessments. Once again, the underlying computations for these services remain unseen and you don’t necessarily need an elaborate online portal or website to access the information. At its most basic form, you can receive the resulting insights in an email with a simple message along the lines of “you will obtain X tomatoes this week” or “your kalanchoe will be ready for sale by date Y”.
- Big data: The devil is in the details
- AI vs automation: Defining a digital future
- Big data helps growers track stress and yield
We are just at the beginning of harnessing the full potential of AI. As technology companies gain greater access to big data, we can expect AI software to conduct an expanding list of complex analyses of greenhouse growing conditions. The following is but a short list of what to expect within the next three years. Gone will be the days of a “guess-and-check” approach when attempting to increase harvest yields and speed up flowering times, for example. Future AI tools will provide scenarios that predict how yield and growth of ornamentals will change if a grower would alter, say, CO2 levels and supplemental lighting in the coming harvest cycle. Imagine how this could automate and streamline fundamental tasks in research and development. Behind-the-scenes analyses of growing conditions show promising results in our abilities to identify areas of a greenhouse experiencing environmental stress that make plants vulnerable to crop diseases and pests. This means we may soon automate many forms of disease scouting. By crunching millions of data points on unseen servers, emerging AI technologies show potential in flagging growing conditions that cause skin cracking and blemishes in produce. The secret to growing the perfect tomato or pepper may thus reside within smart software rather than at the fingertips of a robot.
AI innovations will define new standards in greenhouse farming
With the growing availability of big data in greenhouse farming, automation enabled by AI will set new industry standards for efficiency in operating greenhouses. Expect such advancements in areas ranging from fully automated grow protocols determined at the push of a button and better pricing of produce, to better labour management and more efficient use of energy.
The last example is a case in point. Technology titan, Google, reduced energy consumption by 40 per cent at one of their facilities once their AI software learned how best to operate cooling systems. This groundbreaking achievement required no major equipment or changes to the building’s infrastructure, just smart software able to predict ahead of time when a facility would heat up and by how much. Now with the cat out of the bag, you can expect energy-saving innovations like this to become standard in diverse industries, including agriculture, and influence future policies for clean energy and greenhouse gas emissions.
Though they may lack the shine and satisfying mechanical hum of machinery, it should now be clear that unseen AI tools will play an important role in automating greenhouse cultivation. The time is now for growers to embrace this reality and foresee how technological progress will soon make many tried-and-true processes in greenhouse farming obsolete.
Jason Behrmann, PhD, is the senior marketing communications manager at Motorleaf. He can be reached at firstname.lastname@example.org
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