Motorleaf’s Vision for AI in Grow Operations

Motorleaf’s Vision for AI in Grow Operations

---

How does Motorleaf differ from its competitors?

There are a variety of different companies looking to improve efficiency within greenhouses: lighting companies, substrate providers, farm management software, etc. We are unique because we’re focused on figuring out how you should grow your crops instead of minor efficiency gains. This is a complicated problem that requires the help of a virtual agronomist that can make calculations and predictions that a human brain simply couldn’t. One of the biggest accomplishments we had was when we realized that we had exceeded all of our expectations when it came to harvest yield prediction. We were extremely close in our predictions, weeks in advance. This is important not only because we get a number close to the actual yield weeks in advance, it means that we understand why it is that yield.

---

What are the top three problems you are solving at Motorleaf?

1. We are democratising crop production expertise, via an intelligent, AI-powered platform to learn the best methods for a return on investment.
2. We bridge the gap for equipment and service providers who can’t see how their products and services are actually effecting change in the grow space.
3. We are moving away from static “recipes” for strain-specific crop production and are delivering dynamic growing protocols tailored to your unique conditions.

---

Implementing Artificial Intelligence

---

How does Motorleaf collect and analyze the data it uses?

We collect data via third party environmental controls, our own hardware, IP cameras, and by tapping into customers existing data (which may be available via an API or something as simple as crunching spreadsheets they have used). Data analysis is primarily algorithm-based, but before that happens we have data scientists review the data that they present to the algorithm, just to make sure it’s valid.

---

How is the equipment installed?

We’ve designed our solution to be relatively simple and easy to implement, and we rarely need to visit a customer to install. However, we still like to visit clients locations to get a better feel for what they are doing. For software integration, we’ll sometimes conduct a remote desktop meeting.

---

The Market for AI

---

Where is your ideal target market located? Starting out global or local?

We sell directly to North American customers for the most part. We have customers in approximately eight other countries, but these are mostly to test how a variety of things work and investigate how we can scale in those locations.

---

How do you plan on getting your product in the hands of your customers?

For the most part we deliver our products via direct sales and we have some resellers in Europe for our greenhouse yield prediction and Grow Journal products.

---

Why should customers purchase your products over the competition?

In some cases they shouldn’t. Ideally, they’ll reach out to us and explain what they really want to accomplish and by what date. In some cases, customers buy from us and other service providers that may have some crossover with us, with their data flowing between the two entities.

---

How do you plan on measuring the success of your product(s) in the market?

We have key performance indicators (KPIs) that look at how the products are being used (or not being used) as well as more traditional feedback directly from our clients.

---

Where do you envision the Canadian Cannabis industry to be in the future?

My opinions here certainly do not represent all of our team members or our investors, but there are a few things that seem likely to happen:

1. The sun is essentially ‘free energy,’ so preexisting commercial greenhouse infrastructure will continue to be retrofitted to accommodate Cannabis until supply outstrips demand.
2. Supplemental lighting will help increase production and quality control, but will only reach its full potential when it is paired with a machine-learning intelligence (or AI) to help remove the guesswork. Similarly, because revenue per kilogram will drop over time, each facility will need to become more reliant upon machine-learning to keep their margins healthy.
3. Cannabis production will become a race to the bottom in terms of what producers can charge for their product.
4. Large “traditional” corporations will swallow up the biggest and best producers, adding their own expertise in work flow and distribution.
5. Smoking flower will become stigmatized like smoking tobacco. Adding THC/CBD into ‘socially acceptable’ activities like drinking/food consumption will likely overtake smoking.
6. Heavy metals found in crops will become more understood, and regulations will have to become more stringent.

With consumption becoming more accessible, I think the correlation between mental health concerns in people 18 and under will turn a corner. Speaking from personal experience with my own family, there is a very cavalier attitude surrounding ‘harmless weed’ and underage consumption. As much as I hate to sound like a killjoy, listening to our mental health professionals and medical experts is just as important as listening to those who tell us Cannabis is the new gold rush. If we don’t listen, we’re all culpable if things take a turn for the worse on a large scale.

We collect data via third party environmental controls, our own hardware, IP cameras, and by tapping into customers existing data (which may be available via an API or something as simple as crunching spreadsheets they have used). Data analysis is primarily algorithm-based, but before that happens we have data scientists review the data that they present to the algorithm, just to make sure it’s valid.

A controlled environment. That’s it.

We’ve designed our solution to be relatively simple and easy to implement, and we rarely need to visit a customer to install. However, we still like to visit clients locations to get a better feel for what they are doing. For software integration, we’ll sometimes conduct a remote desktop meeting.

We’re strictly business to business. We’re not equipped to service hobbyist growers at the present time.

We sell directly to North American customers for the most part. We have customers in approximately eight other countries, but these are mostly to test how a variety of things work and investigate how we can scale in those locations.

For the most part we deliver our products via direct sales and we have some resellers in Europe for our greenhouse yield prediction and Grow Journal products.

We have key performance indicators (KPIs) that look at how the products are being used (or not being used) as well as more traditional feedback directly from our clients.

1. We are democratising crop production expertise, via an intelligent, AI-powered platform that uses statistics as well as human expertise to learn the best methods that have the highest chance of increasing an individual company’s return on investment.
2. We bridge the gap for equipment and service providers who can’t see how their products and services are actually effecting change in the grow space.
3. We are moving away from static “recipes” for strain-specific crop production and are delivering dynamic growing protocols tailored to your unique conditions.

---

Why are these important problems important to solve?

1. The percentage of the population that is qualified to look after the production and management of our food is in rapid decline. We can’t wait decades for new farmers to build the experience we need to keep up with global demand. For non-food crops such as Cannabis, the ROI will become hard to keep at current levels, so everything needs to be tightened up, and data-based decisions are the best way to achieve this.
2. Imagine you sell LED’s and you sell their value on a perceived benefit. Once deployed, the lighting company (as an example) has very little data to see a holistic view on the impact their lights have when taken into consideration with all the other variables. How can you improve a product’s effectiveness, or market it with certainty that it does XYZ without this kind of missing information?
3. Without a dynamic crop protocol, the production facility will very likely never reach its potential. That could mean a less than spectacular ROI or a delay in the results of medical science, where crops are being produced for research into disease prevention.

My opinions here certainly do not represent all of our team members or our investors, but there are a few things that seem likely to happen:

1. The sun is essentially ‘free energy,’ so preexisting commercial greenhouse infrastructure will continue to be retrofitted to accommodate Cannabis until supply outstrips demand.
2. Supplemental lighting will help increase production and quality control, but will only reach its full potential when it is paired with a machine-learning intelligence (or AI) to help remove the guesswork. Similarly, because revenue per kilogram will drop over time, each facility will need to become more reliant upon machine-learning to keep their margins healthy.
3. Cannabis production will become a race to the bottom in terms of what producers can charge for their product.
4. Large “traditional” corporations will swallow up the biggest and best producers, adding their own expertise in work flow and distribution.
5. Smoking flower will become stigmatized like smoking tobacco. Adding THC/CBD into ‘socially acceptable’ activities like drinking/food consumption will likely overtake smoking.
6. Heavy metals found in crops will become more understood, and regulations will have to become more stringent.

With consumption becoming more accessible, I think the correlation between mental health concerns in people 18 and under will turn a corner. Speaking from personal experience with my own family, there is a very cavalier attitude surrounding ‘harmless weed’ and underage consumption. As much as I hate to sound like a killjoy, listening to our mental health professionals and medical experts is just as important as listening to those who tell us Cannabis is the new gold rush. If we don’t listen, we’re all culpable if things take a turn for the worse on a large scale.

One of the first jobs I did as a teenager was running a fruits and vegetables stall in my hometown in the UK. I would travel directly to the farms in order to find the best, fresh produce to buy. Later, while I was studying marketing and communication in the UK, I started adopting the concept of indoor cultivation. I learned the complexity surrounding crop production in a controlled environment.

I started learning business development with my career in high-growth tech companies back in 1999, bridging product development with marketing and sales. My work gravitated towards introducing new products and services that resonated with the market. All of my experience developing a business and learning about controlled agriculture came full circle with Motorleaf, a company focused on combining crop production and groundbreaking technology.

Editor’s Note: We’re referring to this press release.

This press release is a milestone that means we can accelerate Motorleaf’s progress. One of the coolest things about this milestone was that just after the round of funding closed, I came back to the office and was introduced to three new hires that had just started at Motorleaf while I was away. This was the first time that we had new staff at Motorleaf brought onboard without my direct involvement. It’s like your baby growing up while you aren’t looking.

Motorleaf started in Sutton, Quebec, which is an agricultural, ski-mountain town near the Vermont border. We presented a TEDx talk about our history, where we cover the journey from a simple idea, to a prototype, to our first small investment and beyond!

There are a variety of different companies looking to improve efficiency within greenhouses: lighting companies, substrate providers, farm management software, etc.

We are unique because we’re focused on figuring out how you should grow your crops instead of minor efficiency gains. This is a complicated problem that requires the help of a virtual agronomist that can make calculations and predictions that a human brain simply couldn’t. We’re taking things to the next level by taking all available data from a greenhouse and connecting it with our virtual agronomist at Motorleaf. Much of our hardware is unique, giving us live data that would otherwise be ignored and forgotten.

One of the biggest accomplishments we had was when we realized that we had exceeded all of our expectations when it came to harvest yield prediction. We were extremely close in our predictions, weeks in advance. This is important not only because we get a number close to the actual yield weeks in advance, it means that we understand why it is that yield. If you understand the why, you can predict outcomes in the future with the right sets of data. You can now ask, “if I do X & Y, what will Z be?”

In some cases they shouldn’t. Ideally, they’ll reach out to us and explain what they really want to accomplish and by what date. In some cases, customers buy from us and other service providers that may have some crossover with us, with their data flowing between the two entities.

Montreal is the center of AI in the world right now. With the talent pool we have on our doorstep, combined with some rather fantastic government programs subsidising salaries/R&D, we can stretch a dollar further than you’d think was possible.