Our Article in Greenhouse Grower Magazine: How AI Learns and How You Can Use AI in Your Greenhouse

[fa icon="calendar'] May 27, 2019 11:32:59 AM EDT / by Jason Behrmann posted in agtech, ai, machine learning, agricultural technology, automation, yield prediction, big data

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 The following is a long-form version of an educational article we published in Greenhouse Grower Magazine.

Here is a link to the original article and below you will find fleshed-out explanations of data and the training of artificial intelligence technology.

All the talk of artificial intelligence (AI) propelling us towards a digital revolution in agriculture is hard to avoid--and with good reason: this discussion is more than just hype. With the fall in computing costs coupled with greater access to agriculture data, a growing slew of high-tech AI tools continue to enter the market. Greenhouse growing is but one sector where growers today are beginning to see real-world impacts from this technology. The time is ripe for us to become more familiar with this novel tool for growers. Let’s begin here with an overview of how the technology works and follow with real-world applications of AI we can use to make greenhouse growing more efficient and profitable.  


Smart software that learns from repetition

Putting to rest the robot misconception is a good place to start: though some equipment and robots have AI features that enable basic decision-making, AI technology is distinct from robotics. Most AI services today involve sophisticated software that works without any machines other than a computer processor.

Sophisticated AI software mimics the most basic way us humans learn: by repeating a task with the aim of getting better and better at a defined goal. Becoming an ace at poker provides an apt example. We begin by learning rules that set limits on what we can or cannot do. The rules make us focus on what is most relevant to achieving the goal of winning by playing the strongest hand. At first, you play a series of poor hands but begin to understand why you lost. After playing more rounds, your follies become less common and you start to learn strategies that improve your chances of winning. With practice, you learn to recognize with a high degree of confidence when your opponents are bluffing and when you have a winning hand.

With AI software we start by setting a basic framework, often termed an ‘algorithm’, that sets the ‘rules of the game’ and a defined goal. Rules can be, for instance, defining the most relevant growing conditions in your greenhouse, and a goal can be predicting the future flowering time for a variety of ornamentals. With the framework set, we now need to train the software to understand how to follow the rules and then make assessments that will likely achieve the goal with a high degree of confidence. Training involves having the algorithm ‘play many hands of poker’ by analyzing data you have on hand. Data can be any information available in a digital format, ranging from spreadsheets of your past heating bills, to records of how fast ventilation fans spin at given times of the day, to measurements of dissolved solids in your irrigation system. Soon enough the algorithm will understand how diverse growing conditions determine the future flowing time for an ornamental plant. Now trained, the smart software can analyze new data from your greenhouse and predict with precision future outcomes for your greenhouse.

AI is remarkable in its abilities to analyze large amounts of information within seconds, which enables the tool to automate common, complex tasks and predict outcomes with far greater precision than us humans are capable of. That said, current capacities in AI are limited. Just like being an expert in poker does not make you skilled at winning chess, an algorithm that predicts flowering time has no understanding of when best to harvest tomatoes. If you only play a few hands of poker, you’ll never get good at playing the game; if you have little greenhouse data of quality, the algorithm will also fail to learn. Thus, if you started cultivating a new variety of crop, you must complete a few harvest cycles in order to have sufficient data needed to develop AI tools for that new addition to your greenhouse. Moreover, applications of AI in greenhouses must be specific and are useless towards completing any kind of creative work that requires the occasional bending of the rules. Now let’s take a closer look at examples of these specific applications of AI.


AI as your digital assistant

Greenhouse tasks that require detailed comparisons or trial-and-error assessments are prime targets for an upgrade with an AI digital-assistant. Diagnosing diseases and pests in your greenhouse is one example. If you don’t recognize a crop illness offhand, you’ll need to do your research in order to identify the problem and know which pest management strategy will stop it in its tracks.

No longer. We now have thousands of digital photos of all major crops plagued with common ailments. Using these images to train an algorithm, smart software can now recognize many crop diseases and recommend treatment strategies. You now can download a free app for your smartphone and then snap a picture of an ailing greenhouse plant to obtain a diagnosis and recommended treatment within seconds.     

Knowing the growing conditions that cause skin cracking and blemishes in vegetables is another problem of particular interest. Six to twelve per cent of a tomato harvest is unfit for the fresh retail market because of cracks in their skin. Growers often adjust humidity, temperature and irrigation factors through a process of trial and error before they find the right mix to control the problem.  Forget trial and error. AI can learn patterns in growing conditions that cause skin cracking and recommend what to adjust first in order to maximize your yield of picture-perfect produce.


AI makes growing precise

Imagine calculating all the financial information for your greenhouse by hand. Not only would this manual task be a frustrating waste of time, you might also feel unsure about the accuracy of your results since there’s a good chance you forgot to carry a one or mistook a scribbled digit of “5” as a “6”. This is why you use spreadsheets and a calculator to do complex math. AI is like a calculator because it too can do complex calculations and avoid errors that pop up from manual work.

Forecasting harvest yields is a case in point. Growers need to know if they will harvest enough produce to meet contractual agreements with their buyers. Predicting the weekly harvest yields of, say, tomatoes and peppers is a manual process that requires counting underripe fruits and making informed guesses as to how current growing conditions will affect the speed at which they ripen. Not only is this a time-consuming process, it’s also often imprecise; errors in forecasts can be off by 30 per cent from the actual yield that week. AI can automate harvest forecasts with greater precision because an algorithm can conduct a continuous analysis of your growing conditions. The algorithm can learn how growing conditions will affect the speed by which vegetables ripen, enabling growers to know weeks in advance their future harvest yields within a single digit percentage point.


AI provides a means to store skills and knowledge

Growers are feeling the pinch from growing labour shortages of both skilled and unskilled workers. Concerns in the industry are mounting that should a member of your team--such as your head grower--retire or get poached by a competitor, it may take quite some time to find a replacement. And when you do, it can take years of training for this new team member to get enough of a feel of your greenhouse operations before they can be a fully autonomous employee.

AI can help ease the pain of losing a skilled employee. Once you train an algorithm to learn a task, that skill will remain embedded in smart software. Recall the previous example of harvest forecasting. Armed with the AI-automated forecaster, your greenhouse can continue this essential task even if you lose your head grower. After you find a replacement, they no longer need to learn how to conduct harvest forecasts, freeing their time to focus on more creative tasks that extend beyond the capacities of AI. Overall, your new employee can learn the ins-and-outs of your greenhouse operations in less time.


Fear not and ask questions

The potential for AI to automate and streamline operations in industries ranging from finance to healthcare demonstrate its broad impact on the future of business. The greenhouse industry is no exception. The time is now for growers to understand how AI will disrupt common practices in growing. Given the novelty of the technology, we are in the early stages of outlining how best to use AI and defining what rules should govern the provision of data from your business. While its novelty does raise questions, it is counterproductive for growers to shy away from this impressive technology. Should you have any questions about AI, let us members of the agriculture technology sector know about your concerns. Rest assured, we are ready to engage in further discussions about how this high-tech tool can assure bountiful harvests in your greenhouse.

Would you like to learn more about artificial intelligence and how this technology can strengthen your greenhouse business? We are ready to answer any inquiries; please forward your request to our Sales and Customer Service teams by filling out this short form and we will contact you soon after.

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Spanish Resources and Customer Support for Artificial-Intelligence Greenhouse Technology

[fa icon="calendar'] March 13, 2019 4:55:00 AM EDT / by Jason Behrmann posted in artificial intelligence, greenhouse, automation, yield prediction

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Given that many greenhouse growers in North America have subsidiaries in Mexico and Spain is a leading greenhouse nation, we are excited to announce new Spanish resources for our customers in these regions. 

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Better Prices for Your Produce: Hard Numbers for the Value of AI-Automated Harvest Yield Forecasts

[fa icon="calendar'] March 5, 2019 2:40:16 PM EST / by Jason Behrmann posted in greenhouse, tomato, automation, vegetable, yield prediction

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Each greenhouse is unique. Your cultivation methods, equipment, and size of workforce and facilities are but a few factors that make you distinct from your nearest neighbouring greenhouse. Given the unique attributes of your business, it is challenging to calculate the exact value and return on investment (ROI) your greenhouse will obtain from Motorleaf’s AI-automated harvest forecasting services unless you provide us with detailed information.

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VIDEO: Our AI for Greenhouses Takes the Stage at Leading Produce Trade Show, Fruit Logistica

[fa icon="calendar'] February 19, 2019 5:22:23 PM EST / by Jason Behrmann posted in ai, greenhouse, agricultural technology, automation, trade show, cultilene

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Growers used to ask, "does it work?" Now they ask, "how does it work?"

The organizers of the leading international produce trade show, Fruit Logistica 2019, selected Motorleaf as one of 20 most impactful new agriculture technology companies to present their innovations on stage. We grabbed the attention of many in attendance, including several media outlets.

Here you can watch one of our interviews summarizing our AI automation services for greenhouses we displayed at the event, between journalist Chris White (CW) of Fruit Net Media International and Dr Jason Behrmann (JB) of Motorleaf. If you prefer to read, below you will find a transcript of the interview.



CW: Hi, good morning. Welcome to the media studio at Fruit Logistica. I’m Chris White from Fruit Net Media and I’m joined this morning by Dr Jason Behrmann of Motorleaf. You’re a Canadian company, Jason, a newly established start-up running for a couple of years; and your business is all about artificial intelligence and glasshouse vegetables, is that right?

JB: Yes, that’s true. So, our company Motorleaf—I oversee marketing and communications—the company was established in 2016 and we specialized in artificial-intelligence-enabled automation for the production of tomatoes and peppers in greenhouses.

CW: So what does that mean: ’Artificial-intelligence automation of production’? I’m intrigued.

JB: Well, instead of using robots and other forms of machinery, you can use very intelligent software to automate important tasks in greenhouse farming. So, our primary service right now is doing highly-accurate yield prediction, meaning forecasting the exact amount of tomatoes and peppers a greenhouse will produce weeks in advance on a week by week basis.

CW: And I can kind of see the advantages, but tell me what they are for this application of this technology for the grower.

JB: Well, using big data of the growing conditions within the greenhouse we are able to develop incredibly smart software that can predict within a few percentage points of the actual yield a greenhouse will produce on a week-by-week basis. So, it’s the first time in history that a farmer will know just how much tomatoes or peppers they have so they can better plan their labour; they can better plan promotions and marketing initiatives well in advance; and they can better plan ideal pricing for their produce; and they will know whether or not they’re going to under- or over-produce to meet contractual agreements with their buyers so they can better plan if, you know, something goes wrong and make sure they keep that really great relationship with their buyers.

CW: Now, the big advantage you have, of course, is that you’re growing in a controlled environment so you know what the inputs are, you can literally predict day-by-day, and determine day-by-day the conditions for growing are going to be. That’s presumably why this works, is it?

JB: It’s one of the first times in history, now, with all the different types of technologies and machinery and also different types of sensors, we have a wealth of data available now where we can gain a fundamental understanding of the ins-and-outs of greenhouse farming and that’s what we can now use to develop cutting-edge automation—artificial intelligence technology. Before, we did not have access to this data and also now with all the data we have, a lot of people are scratching their heads, especially the farmers, saying, “ok, well what can we do with it?” Well, the ‘what’ is artificial intelligence.

CW: And that’s taking big data, essentially, and condensing it down and giving people the kind of information they need in terms of being able to predict what is coming in terms of production.

JB: Yes. So, the services we offer is a turn-key, full-suite of services where we will develop custom-made technology for each greenhouse and each variety of tomato or pepper they produce. So, we handle all the ‘mess’, let’s just say, all the complex computer technologies, and then we will be able to convey that information to them in terms of weekly yield simply be communicating through any kind of digital format; it could be as simple as an email. And the additional services we are developing now are automated disease scouting; so, we can conduct are very detailed analysis of the growing conditions within a greenhouse and identify zones that are experiencing environmental stress, like extreme heat or dryness, and that makes plants vulnerable to disease. Armed with that information we can tell growers, “hey, look out in that area, intervene early using fewer pesticides and sacrificing fewer crops to avoid the problem.” Another product we are developing and will be on the market soon is detailed analysis of growing conditions that cause slight imperfections in tomatoes and peppers. So, right now, about six to 12 per cent of a harvest cycle is lost due to small imperfections in the fruit, such as skin cracking—

CW: And ‘loss’ means lost money as well, of course. 

JB: Absolutely lost money, and also contributes to food waste, which is a monumental problem that many people are aware of now and there are new regulations throughout Europe, for example, that are trying to quell this problem. And with our data analysis we can identify factors that will cause those small imperfections and inform growers what they can do to avoid the problem altogether, increase their profits and also avoid crop loss.

CW: Sounds like a win-win. Now, your are based in Quebec, in Canada, and yet you are working with growers all around the world now.

JB: We are based in Montreal, Canada, which is an international hub for the development of artificial intelligence technology. And we currently have clients in Japan, Tunisia, Europe, Canada and the United States; and I’m very excited to announce that last week we just established a new partnership with Cultilene. Cultilene is one of the world’s largest suppliers of glass substrates and substances for greenhouse growers, and though this partnership we’re going to introduce our artificial intelligence technology to their client base, which is in, oh, spans over 50 countries. So, we believe we can introduce AI into greenhouse farming throughout the world within a short period of time.

CW: So, it sounds like the future of farming very much is about AI.

JB: The future of many industries is going to be heavily disrupted by this technology. It’s really ground-breaking. Farming is just one sector, and with the growing slew of services that we offer greenhouse growers, I am very confident that we can make a significant, positive impact in this one industry.

CW: And you use the word ‘disruption’. Disruption always sounds to me very negative. Sounds what you’re talking about is extraordinarily positive.

JB: ‘Disruption’ is a very positive term. It’s providing new ways of doing old forms of industry and making current industries far more efficient and far more profitable and accessible. And artificial intelligence checks all those boxes.

Cw: Excellent. Thank you and good luck.

JB: Thank you very much.

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PRESS RELEASE: Automation by Artificial Intelligence Aimed to go Mainstream in Greenhouses with Dutch-Canadian Partnership

[fa icon="calendar'] January 31, 2019 8:01:00 AM EST / by Jason Behrmann posted in ai, greenhouse, automation, "press release", cultilene, partnership

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