182: The Importance of Smart Controls in Transforming Greenhouse Operations

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So Gretchen and Neta, no strangers to the Vertical Farming Podcast. Thank you

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so much to both of you for joining. I know I've been in conversations

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with Neta about an opportunity to come back on the show, and she mentioned some

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work you guys were doing together. So just to kick things off, Neta, you want

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to kind of talk through what prompted the desire to come back

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on and share what you've been working on? Yeah, absolutely. Thanks again for having us

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on the show. It's good to always chat with you again, Harry. It's always,

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we love chatting with you because it's this organic conversation where you

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really highlight what's going on in the industry. And what really prompted me to come

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back and have a conversation was to shine some light

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on a project that we worked with ERI. Gretchen

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brought us into a project which was called the CalNEX Project, which was

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really taking a look at the energy consumption that's being used in the

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greenhouse on operations and really understanding

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does adding more environmental monitoring and controls actually

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make a difference from an energy usage perspective? This was

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the first study, from what Gretchen described to me, that was really

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being looked at from a very scientific perspective. And having had a science background,

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we were really excited to be a part of this project, which is really looking

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at things side by side, kind of what we call the smart room

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versus a smarter room over a long time period. So really exciting to

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be back on this show to talk to you guys about what the findings were

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and what Gretchen's team and was able to learn from this project.

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Thanks for that context. And Gretchen, I think what would be helpful for the viewers

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is to kind of share how long you've been involved in horticultural lighting research

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as some context leading into this collaboration. Yeah,

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sure. So in 2021, I was on a team

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that worked with one of the largest utilities in North America, Commonwealth

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Edison, that serves like Chicago and a lot of areas in

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Illinois. To explore what they called the opportunities in controlled environment

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agriculture. And so at that time, I started this theme of looking into

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and uncovering the benefits of LED lighting and automating those

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systems. And not just the energy benefits. At that time, I was actually looking at

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what the utilities call non-energy benefits. So I think that's what growers

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often care about more, right? Is, yeah, energy savings, but also what

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else? What happens to the plants? What happens to the the labor benefits and other

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things like that. So then in 2023, I took on the part-time role of being

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executive director of GLAZE, the Greenhouse Lighting and Systems

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Engineering Consortium at Cornell University. And I got to collaborate with amazing

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scientists, still get to collaborate with amazing scientists at Cornell

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University, Rutgers University, and Rensselaer Polytechnic Institute.

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And at that time, I was helping them complete this NYSERDA-funded

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greenhouse lighting research. Which took a look at not just the beginning

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of LED lighting adoption, but also the adoption of

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dynamic lighting controls. And that's where we're going as I get to the

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point of where I started to work with Netta. So in 2023,

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you know, I had been running into Netta in the industry quite a bit, and

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we collaborated a bit when I was at Resource Innovation Institute. But in

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2023, now at Energy Resources Integration, I applied for funding

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like we all try to do. We try to get some funding from someone else

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to do something cool. So I applied for funding from a California utility program

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called CalNEX. And what they do is they explore energy efficiency

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technologies and vet them for inclusion in rebate

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programs, right? So how can we get new tech to get money so

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that growers can adopt it, so that any business can adopt it? So from

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2023 to 2025, Neta and I collaborated on the Smart Controls

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Project, where we explored how these dynamic

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controls for lighting, climate control, as well as energy monitoring, like Netta mentioned,

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really prove out the energy benefits so the utilities would give out rebates,

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but also prove out the benefits for the businesses so that they would want to

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do it too. So we completed 4 field demonstrations at 3 different

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farms in California. And, you know, this year we're really running

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around celebrating the results, which is that it proves that these

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will beneficial and that utilities should provide rebates. And hopefully

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the wave will just be beginning now of rebate programs

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offering more for controls, and more research and education

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about this will continue to see growers adopt it more. So that's my history for

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the past 5 years in this field, and I really loved the past few years

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being able to collaborate with NETA. Sounds like you guys had a— it's like a

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match made in heaven in terms of what NETA's been working on Neda, what made

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Microclimates a good fit to support this project with CalNext?

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Yeah, I'd say the biggest thing was probably that we are

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unique in the sense that we're not trying to replace any environmental

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control system. Our job is to complement what's already

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there. And the projects that— the locations that

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Gretchen mentioned, every site had something different. One of them had

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a Ritter control system, a very robust, great company. Another

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one had a Priva. Another one didn't really have much

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automation. So, our job wasn't to go in there and just say, rip everything out

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for this control study and start over because we want to collect the

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data. It was really to take a look at what do you currently have, how

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can we complement that with adding more environmental insight,

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environmental visibility to that operation, and is there a way that we can integrate

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what you currently have? Will that control system allow you to integrate

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And what does that look like? So it really just sort of demonstrated how additional

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environmental monitoring and not ripping and replacing can really bring a lot

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of value. And what did that experience teach you about what is

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being collected now? Was there anything surprising when you went in?

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Yeah, I'd say the biggest surprise wasn't necessarily that they

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weren't collecting data because they were all collecting data. It was really

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the biggest surprise and biggest lesson was that they didn't have enough environmental

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visibility. So, what I mean by that is that a lot of these

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operations had maybe one temperature humidity sensor hanging

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in the middle of the room representing the entire greenhouse or a section of the

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greenhouse. And your control system is only as

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good as the information is taken in, like the input, right? So, it really shined

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a light on the fact that they didn't have enough environmental

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visibility. And the perfect example that I love referring to

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is Floricultura, one of the sites.

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Their crop is an orchid. So they're growing orchids, very

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finicky plant, as we all know. Yeah, very finicky. But they really just didn't really

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understand the environmental temperature, humidity

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environment that the crop was actually experiencing at a root zone

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level, below the root, above the crop, above

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their screens. So that was really important. And we have a grower there

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that's very data-driven, data-rich. He really

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understands orchids. He's probably world-renowned for his understanding of orchids.

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And having that insight was so valuable to him. And Gretchen

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can talk a little bit more about what the outcome of that study was, but

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that was a kind of aha moment that we had, was, yes, they're collecting

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data, but they don't have enough crop environmental

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visibility. And then the question that really came for us at the end was,

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if they're not collecting enough data as it is, How's this world going

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to prepare them for AI, which we can talk about later? But that really got

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us thinking from an AI perspective as well. So yeah, she's teed that

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up for you perfectly, Gretchen, the experience with that. And I'm also curious how you

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pull in all these learnings that you have from all these other locations

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you've been at, all this other research you've been doing on smart controls. And I'm

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curious what your perspective was specifically with that company that Neda

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mentioned. And then what comes to mind for me is like, Does the then grower

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have to think about, oh, I need more sensors now to capture all these different

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locations and all these different places? And I'm sure there's concerns there as well. Yeah,

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I think the— I'll reiterate Neta's surprising moment as

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well of realizing that the field demonstrations are

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using pretty static controls or pretty basic

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controls. So that presented an opportunity for us. I

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think that in the floricultura example, They have a sophisticated

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control system, but what they don't have is a lot of feedback loops

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telling them what's happening at the grower level. The system knows, and it

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adjusts the screens, and it might adjust the lighting, but they aren't perhaps getting

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the info where they can find fault detection. Another site at the

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lettuce greenhouse, the load shape of the lighting circuit looked pretty much the same

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every day, even as the seasons were changing. And so my job as an engineer

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when I'm validating a technology for a utility is, what's the

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baseline? What would they do if we didn't affect anything, if we didn't

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offer a rebate? And so for me, this helped us prove

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that scheduling lighting with time clocks is an industry standard

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practice. And so utility energy efficiency programs

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could help growers save even more energy by using sensor-based controls. And

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instead of just using schedules, they could use sensors to control their lights. With

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floriculture, they're not using lights as much, but with lettuce, the lighting

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was much more of an important thing. So with mushrooms, one of our other

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field demonstrations, the humidity and the climate values were much

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more important. So it's sometimes what we found was surprising

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because it was like, there's a great energy savings opportunity. And then sometimes what we

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found was more surprising of like, wow, there's a real big like information

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gap here that Netta's system can help fill by just providing

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things like trends. Because, you know, I'm used to commercial buildings

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where you can go back, open up the computer log, see months of

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trends, see what all the office building temperatures were in all the different

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rooms. But some growers we found, all they knew from a

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climate level was something that was at a very, like Netta said, one sensor

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in one place. And then all they knew from an energy level was the utility

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bill. For the whole greenhouse. So, Nada was helping us piece apart,

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okay, well, this is actually how much is going to lighting, and this is what

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the lighting's doing. This is how much is going to fans, and pumps, and all

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the other stuff that sometimes is more energy-consuming for a different type of grower that

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doesn't use lights. So, Nada, I imagine a lot of this information

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you've been learning in your interactions with your clients, and the folks you've been

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working with, and all the facilities you've been able to have access to. But, how

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much of this was a learning for you, and because of this partnership, because of

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the in-depth visibility you had to what was there already,

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and maybe some preconceived notions about what people think growers are

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measuring versus what's actually happening in these facilities? It was

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quite a surprise. I think I had the vision. I assumed that

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they were collecting a lot of data, and then we, I think, also made some

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assumptions that they understood a little bit about their energy consumption, at least

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maybe like— because we, we've heard this over and over and over again the past,

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you know, 10 years— overhead costs are too high?

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What happened to the company that just started and they spent millions and raised

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millions, hundreds of million dollars to build this beautiful vertical farm? And why

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did they go out of business? Over and over again, the theme that we have

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heard is that overhead costs, overhead costs. And we know that 70%

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of their energy consumption is related to their HVAC

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systems and their lighting system. So I think I had made the assumption that they

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must understand something about their energy consumption. And even the companies

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that had a whole sustainability team. We started out with one of them that had

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a whole sustainability team dedicated to it. They really cared

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about this topic, and they knew all about it, but they really

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weren't collecting energy data at a circuit level,

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at, let's say, a pump level, at a lighting

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level. So, it was an aha moment of they actually don't

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have visibility into that data, into how much energy is

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being used. And if you don't have that visibility, How are you going to make

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small changes so that you can reduce that overhead cost? Is that

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common, Gretchen, from what you've seen? Obviously, you've done a lot of this research a

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lot. And I'm also curious if there's a difference between controls that

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need to be monitored on the greenhouse side versus pure vertical

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farms, and if you have enough data to kind of back that up.

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Yeah, I appreciate you bringing up that there's maybe different baselines for

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greenhouses and vertical farms. And if you check out the results of my study

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with Netta, If you look at what we did was we created a tiered

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hierarchy of control sophistication levels. So level 0 is

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basic. Level 0 is manual. Level 1

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is when you start to have some basic controls like a

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timer. And then when you start going to level 2, that's when we start to

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have some sorts of an automation system going on, but they're not

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talking to each other. And then as you go up the levels, you start to

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have actual system integration. And what we tried to do with the study was for

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every system, lighting, HVAC, and irrigation, what

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level are they? So we did surveys, we did interviews, site visits,

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and field demonstrations. And when you take a look at the levels of

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all of those systems, you can't say that there's a common level that

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you see amongst all those systems across greenhouses and vertical

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farms. We observed cannabis indoor farms that

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In some cases had manual lighting controls, which boggled my mind, but that's what

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they chose to do. And then we also had one of the most, what they

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touted to be the most sophisticated, completely integrated, completely

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like well-oiled machine-controlled cannabis farm. We went

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to a huge cannabis greenhouse, which had very little lighting and

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therefore did not need a ton of advanced lighting control, but had one of the

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most sophisticated irrigation systems we'd ever seen, where they did

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gravimetric weighing of their plants to see how much water was happening for

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a sample plant that therefore then dictated how much water the rest of the crop

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got. They also had sophisticated energy generation

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systems, cogeneration systems, and absolutely knew a ton about their

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energy. So that's cannabis, and that's just how you can see, like, you've got the

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full spectrum there. It's hard to say what the baseline is. Some people

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have said that when you've seen one greenhouse, you've seen one greenhouse. So in my

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study, I've seen many, but can we say that my study

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applies to everyone? No, it probably just applies to California. But the last thing

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I'll say is that I do think that overall, greenhouses, if they're growing

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a crop that has a lower profit margin, we generally saw have less advanced

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controls. And if you have a crop like we've talked about with

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orchids, there's more of a reason to have some more Cadillac-level

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integrated system. But I think what we do see overall across all of

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them was still generally a fairly low level of system

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energy monitoring. So they've got an idea of how much they're paying

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for their bills, but they— most of them all across the board

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did not have the ability to say, yep, I've been spending $20,000 on lighting,

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$40,000 on HVAC this month, and then in total my bill was

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$80,000. So yeah, that's sort of the gist of it, is like

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the study helped us put more dots on the map, but we still have more

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studies that still need to keep proving that

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there's a general trend for a particular crop. I

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can't really say that yet. So with all this data that you now have

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available, Neta, I'm curious how you think about approaching

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partners to work with. Does this change your thinking about maybe doing an

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audit first about seeing where they're at and what exactly they're measuring? Because

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to Gretchen's point, they may think they've got a robust monitoring system,

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and with the proper audit, you can almost pick out like where

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There are gaps in how they're thinking about this. And then obviously one of the

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questions is going to be, is microclimates going to compete with the systems

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or is it going to work or is it going to complement them? So I'm

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sure those are questions that come up as well. Yeah. So I think what you

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mentioned about the audit, yeah, I completely agree. Gretchen's team and

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Gretchen's expertise and ERI is the perfect company

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that can help with those things. They could step in and take a look at

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an operation and audit them and understand Where, you know, you can even— I

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believe, Gretchen, if I'm not wrong, you guys can even audit their energy bills and

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see if there was some mistakes on their bill. So there's a whole level

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that ERI and Gretchen's, especially Gretchen's expertise, can really

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step in and help operators right away without having to install

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anything. They could just look at documents and papers and bills

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and make sense of it. That's number one. Then number

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2, what you mentioned, Harriet, is Competing or

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complementing? Well, historically, I'd have to say that most

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companies out there have been about replacing, right? We actually come in and

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complement. We're not asking to rip and replace. The other thing that's

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really important is that these energy monitoring systems have to be easy

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to install. If they're not easy install and there's going to be a bunch

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of wires everywhere, it's unlikely that the operators want to go through the

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hassle. And that's where I think what we've done at Microclimates is really

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try to simplify that process. where we bring in a computer with our

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EnvOS, which is an environmental operating system, and these wireless

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sensors for energy monitoring that just connect, hook to the

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circuit level, and quickly begin to monitor your environment. And

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then the operator also has the ability to put in their scheduled

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pricing. So then automatically the dashboard will show you

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how much energy did this pump use How much did it cost

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me? And you have all these beautiful graphs that you can look at and make

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sense of it. Then you start working with a company again like ERI

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and Gretchen's team, and you say, okay, now that you've reviewed my

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baseline bills and what I'm doing, and now that

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I've added a few circuits, I have 3 months of

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data, 4 months of data. Maybe you want to have 1 year of data depending

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on your crop, maybe seasonality, who knows. Now,

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Gretchen, what do you suggest I do with this information? And that's where they

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come in and help you fine-tune your system slowly. And again,

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it doesn't have to cost very much. It's not like we're spending hundreds of

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thousands of dollars by any means. It's very reasonable

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to get to that point and shave off 5% off your energy

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bill, which makes a huge difference. Yeah, Gretchen, I see you

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nodding your head. So what's usually the responses when you show them these studies?

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And obviously, there's always pushback when you have to change systems that are

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already in place. And I'm curious, what are some reasons you'd give a greenhouse

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operator for reasons why they should switch to

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daylight-responsive lighting controls, for example? Yeah, or

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daylight-responsive lighting controls plus some more energy

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monitoring. Exactly. I think that the things that both

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Nada and I try to do is take one step at a time. So yes,

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There's small steps to take, like doing a bill audit or doing

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a quick audit of like, what do you even control? What do you even

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measure? And then I would be able to share with Neta, okay, they

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have this much information. And what we can do together is provide them with

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more valuable information on top of what they already have. And

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then also provide the right thing, whether that's

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light, the right light, or the right humidity, or the right temperature. Right.

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And then lastly, the thing that I care about, save energy. So get

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more information, get the right target hit, and save

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energy. Hopefully save money because everyone's energy costs are going

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up. And so the payback period just gets better the more you implement

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automation. So we found that sometimes growers didn't know how much light

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they were actually receiving, right? So they can then tailor their

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controls to meet production goals, influence plant quality

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or adjust the amount of light they're doing because they were never actually meeting DLI

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target, which is something we've found in quite a few greenhouses we've worked with.

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Then they can use, you know, automation to either

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do some sort of simple algorithm or use AI, which we might talk about a

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little more, to decide how much light should I give at any given, you know,

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15-minute interval. Should I add a little more light because power

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costs are going to go up tomorrow? Do we have a production goal we haven't

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met yet? So there's some things that people can start to do to provide light

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when it's needed or provide, you know, turning it off when it's not needed. And

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then lastly, you know, the pocketbook. I think that we haven't talked about the numbers

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yet, but I've been really excited because my study has sort of stood

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alone along with some academic studies that have been done by like University of Georgia

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and Cornell University for a while, since like 2016. These

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academic studies plus the CalNEX study have said automation

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saves a good amount of energy. And usually that raises

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eyebrows and suspicion because large numbers usually mean like, how can you really trust

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that you're gonna save 50% of my lighting energy? That's

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crazy. But it's not actually crazy. My study, as well as those

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recently performed by other teams, so the ASHRAE

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Standard 90.1 committee just this past month presented

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a slide that showcased 42 to 62% energy savings

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compared to time clock controls when you implement daylight responsive

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controls. So if you're paying over 10 cents a kilowatt hour, which

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many of us are, many of us even at our house are paying more than

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10 cents a kilowatt hour, the payback can be as short as months.

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And I heard someone in the ASHRAE committee say this

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may be the quickest payback measure that we've ever added to

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90.1. And so I really think that speaks to not just

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the impact, the magnitude of this opportunity that

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for the CEA industry, but overall for the whole buildings industry,

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this is a very big opportunity in terms of energy savings. So that's what

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I'd say to a greenhouse owner, but also I'd say to a vertical farmer as

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well. I'd say, what do you currently not know? What do you want to

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hit for your target? Do you know you're hitting that target? And then let's save

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some energy. Even if you're indoors, you're probably able to dim your lights

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in ways that you're not. You're probably able to modulate your fans, your pumps.

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There's always those tweaks, the continuous improvement that Netta was talking about.

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5% a year, that'll help you cushion yourself from utility

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cost increases. And then if you go even more aggressively, you know, if you have

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a dedicated energy management practice, you could be saving more like 15%, 20%

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a year. Or you could go big with one thing and

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over the years save 50% energy savings with larger construction projects.

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So naturally, that begs the question, Gretchen, how come people aren't installing these

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everywhere? And I'm excited to hear what Neta says

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about this one as well. But like, the quick, simple payback does not tell

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the full story of a construction project, right? Like, if it did, then you and

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I would be doing improvements to our living spaces all the time

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because the payback would be the reason we would just do it. But a good

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reason, I spoke with a grower in the Mid-Atlantic region who still uses

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high-pressure sodium lights and time clock controls, and they

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explained that they know every reason under the sun why they should install new equipment,

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but the disruption to production is just a challenge to orchestrate that they—

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I'd say the number one reason is operational impacts during construction. The

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second one I'd say is these systems are not yet industry standard practice because

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they're not required by codes or published in standards. But as I mentioned,

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ASHRAE adding 90.1 language to require daylight responsive

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controls means that, that because it's the basis for codes and standards around

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the world, anyone could point to that and say, okay, Minnesota

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chooses to adopt this. Okay, you know, Maryland chooses to adopt

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this, or even the city of Chicago chooses to adopt this.

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But it's happening in California at the state level right now. So I think that

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if you're a California grower, This is the most important thing to know is that

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it's not industry standard practice now, but by 2028, you're going to probably

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be— January of 2029— required to do it. And then

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lastly, upfront cost, right? Even if you get paid back, even

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if you get a rebate, you've got to pay for materials, labor, commissioning,

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training. And that's just a little bit too much for a lot of businesses to

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tackle right now, especially during uncertain economic times. And,

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you know, Henry from Agritecture posted, just today on LinkedIn about

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how, you know, things are consolidating. So those

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who are going to do these projects are going to be the, probably the larger,

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more historically established companies. Yeah, that trough seems

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to be a bit deeper than people originally thought. Yeah. Anything

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to add on that? Gretchen covered it all. Like, I'm in complete agreement

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that it is. First of all, you know, she mentioned California and oftentimes we do

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see that states follow California. So that's something to say. I think it's

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going to happen over time. More and more states are going to adapt what California

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does and you see Historically, we've seen that in every industry, including the food industry

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that I was in prior to this. So, I think that will happen. I do

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think that interruption is really hard for them, and they just don't have the manpower.

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Everyone's limited right now. You know, you don't have the manpower to make those changes,

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and interruption to their day-to-day operation is a challenging

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one for them. But we need to make this accessible too. I think that if

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we can make it small and accessible and you start one room at a time,

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it's possible to get there. It's just that you don't have to overhaul the

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entire operation. You can start with one room and say, I changed

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the lights in this room, or, I applied DLI in

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this one room. What was the impact of that over the next 5

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months? How did that compare to my last 5 months? And if that works, then

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you slowly transition the change. Yeah, and I think it's important, and

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maybe you can talk to this a little bit more, Neda, about this ability for

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growers to understand that they don't have to rip and replace, as you say. And

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I think they appreciate having the freedom to choose a mix and match

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if that fits their needs in terms of sensors and tech. So how

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does this help you think about how to approach established growers

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who are set in their systems like the example Gretchen outlined?

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Yeah, every greenhouse is different, right? Every greenhouse has different

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needs. No one manufacturer makes the best sensors on the

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market. We know that the technology

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changes. So, I'd say it's, you know, our philosophy

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has been you don't need to rip and replace, you can complement. So,

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it's really coming in as a consultant and understanding from that

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operator, what is it that you have today? Where are your

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blind spots? Where do you want to go in the future? And

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how can we complement what you currently have? So, in other words, let's say

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back to DLI, let's say that You want to save

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some energy on your lighting, but you don't have

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DLI. You're worried about the expense and all the

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wires that are gonna run around with all these different PAR sensors. How can

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we address that? And the way we've addressed that is, what if you get a

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PAR sensor that we can convert to a LoRaWAN wireless

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so you can move your PAR sensor around? Now you don't have a bunch of

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wires. Now you have flexibility to move the sensors around. Right. What

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if you use wireless sensors for temperature, humidity? We actually have a

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facility right now in Virginia that's doing some study on their blind spots. You can

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actually deploy some wireless sensors to get to know those blind spots,

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and you can still take that information, feed into your control system. Whether the

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2 control systems, their existing control systems that we're not ripping and replacing, can

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be integrated with microclimates is a different story. But if they can be

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integrated, then the 2 systems can talk to one another. So really, it's

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about giving them the freedom to choose what's right

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for their operation. So I'm a strong believer that, you

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know, growers shouldn't— they should really own their own

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strategy, and they shouldn't have any limitations because of the vendor's

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limitations. And I think it's really, really important that they have the freedom to

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choose. That's helpful for that context. Thank you. So Gretchen, you did

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touch on AI, and I'll give you both a chance to talk on it and

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seeing what's coming up. But based on, you know, what you've seen in your experience

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with this research, And obviously following the trends of what's happening on the AI

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front, where do you see the biggest disruptions happening, or what should growers

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be preparing for? Well, I want to reiterate what Neta said about a phased

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approach. So no matter what happens next, whether there's this

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great new innovation like a brain that will tell your control system

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exactly what to do because it knows the weather for the past 50 years and

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it's predicting the weather for the next 20 months, And that

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would be awesome, but that's like also probably going to be like buying a very

442

00:28:12,610 --> 00:28:16,370

expensive thing for a while. And so a phased approach, no matter what, will probably

443

00:28:16,770 --> 00:28:20,530

be the best. And I think what, you know, the microclimate systems offer

444

00:28:21,010 --> 00:28:24,690

is literal like card by card. So you can decide how much

445

00:28:24,850 --> 00:28:28,370

equipment do you want to monitor and control with the system versus

446

00:28:28,770 --> 00:28:32,370

letting your existing system continue to control some things and monitor some things.

447

00:28:33,090 --> 00:28:36,700

And For example, with AI, if you want to first implement

448

00:28:37,020 --> 00:28:40,860

a simple daylight responsive control algorithm and say,

449

00:28:41,260 --> 00:28:45,100

turn the lights off when the PPFD gets too high, all right, just don't

450

00:28:45,100 --> 00:28:48,780

overlight. Then you take a look at the crop, you say, that looks good. All

451

00:28:48,940 --> 00:28:52,460

right, let's try now a DLI target, which is just based off of the target

452

00:28:52,700 --> 00:28:56,060

you think I'm gonna reach at the end of the day. That doesn't require AI.

453

00:28:56,620 --> 00:29:00,380

It could, but it doesn't need to. Folks from Cornell have been writing those algorithms

454

00:29:00,380 --> 00:29:03,930

since the '90s. And those are just intelligent algorithms. But

455

00:29:04,090 --> 00:29:07,210

AI, I think taking more of the agency

456

00:29:08,010 --> 00:29:11,850

of making decisions might be where we start to see it happen, where it's like,

457

00:29:11,850 --> 00:29:15,530

all right, the AI's gonna start to tweak your DLI targets day

458

00:29:15,690 --> 00:29:19,530

by day because it's decided what you need. And we've allowed

459

00:29:19,610 --> 00:29:23,450

that to happen with other things like screen operation. We've decided that, you

460

00:29:23,530 --> 00:29:27,210

know, the control systems know what's best for the screens as they read

461

00:29:27,450 --> 00:29:31,240

the climate. Responses, like what the temperature is and the humidity is, and they open

462

00:29:31,400 --> 00:29:35,160

and close. So I do think that we'll start to see that integrated more and

463

00:29:35,240 --> 00:29:39,080

more as we let the growers spend their time

464

00:29:39,320 --> 00:29:43,080

doing the more human-valuable tasks. We've started to see the value of human

465

00:29:43,320 --> 00:29:46,040

labor compared with AI labor, and that's where I think we'll start to see the

466

00:29:46,120 --> 00:29:49,640

balance in the vertical farm and the greenhouses. What's important for AI to do because

467

00:29:49,800 --> 00:29:53,240

it's cheaper, and what's important for the grower to do because it's much more expensive.

468

00:29:55,550 --> 00:29:59,070

And Neda, what are you seeing from your side? First, I want to completely agree

469

00:29:59,230 --> 00:30:02,910

with what Gretchen said, that phased approach, right? I love working

470

00:30:03,070 --> 00:30:06,190

with Gretchen because we both have this mentality of things

471

00:30:06,910 --> 00:30:10,750

don't happen overnight. It's a phased approach, and you want to make it

472

00:30:10,910 --> 00:30:14,750

so it's accessible to the operator and it's scalable

473

00:30:14,830 --> 00:30:18,590

so they can slowly work their way there. So from an AI perspective, I

474

00:30:18,670 --> 00:30:22,380

think that AI is actually going to be very different than what

475

00:30:22,460 --> 00:30:26,300

most people expected right now. A lot of people are thinking of AI as this

476

00:30:26,700 --> 00:30:29,980

autonomous growing or yield prediction.

477

00:30:30,540 --> 00:30:33,020

And yes, we're going to get there, but I think we're going to get there

478

00:30:33,180 --> 00:30:36,780

slowly. And what Gretchen touched on is maybe making those

479

00:30:37,100 --> 00:30:40,780

slight modifications in operation, right? To make those slight

480

00:30:41,500 --> 00:30:44,860

modifications to your DLI or your HVAC systems,

481

00:30:45,420 --> 00:30:49,150

AI needs to have the ability to Yeah. If

482

00:30:49,230 --> 00:30:52,830

you don't have the data today and you're not collecting thousands of data

483

00:30:53,070 --> 00:30:56,590

points, if you're not talking to an AI and actually

484

00:30:56,910 --> 00:31:00,030

getting this— I think of the AI today as an assistant

485

00:31:01,070 --> 00:31:04,910

that you hire that eventually is going to become your consultant or

486

00:31:04,910 --> 00:31:07,470

it's going to do the work for you. It's either going to advise you, it's

487

00:31:07,470 --> 00:31:09,950

going to do the work for you on your behalf, right? But if you don't

488

00:31:09,950 --> 00:31:13,550

have the thousands of data points, if you can't summarize the trends, if you can't

489

00:31:13,550 --> 00:31:16,990

make any recommendations and alerts, and you can't generate those reports based on the thousand

490

00:31:17,230 --> 00:31:19,850

data points, how is this AI How am I going to actually get to know

491

00:31:19,930 --> 00:31:23,610

you? So first of all, we have to step back and say, are operators actually

492

00:31:23,930 --> 00:31:27,770

collecting data? We already said early on in this conversation they are,

493

00:31:28,090 --> 00:31:31,850

but they don't have enough environmental visibility, which means they actually are not collecting enough

494

00:31:32,010 --> 00:31:35,770

data, number one. Number 2, is the data coming together in

495

00:31:35,850 --> 00:31:39,370

one place, or are these all different silos and the data is just

496

00:31:39,690 --> 00:31:42,650

disparate and all over the place? Are you able to pull the data into one

497

00:31:42,810 --> 00:31:46,180

place? You should be able to. So that's number 2, is that you don't want

498

00:31:46,340 --> 00:31:49,540

your AI to be working inside of those. You want your AI to step back

499

00:31:49,620 --> 00:31:53,140

and look at your entire operation and make decisions for you. Number

500

00:31:53,460 --> 00:31:56,580

3, can your AI agent talk to other AI

501

00:31:56,820 --> 00:32:00,660

agents? Some can, some can't. So you need to have an AI agent

502

00:32:00,900 --> 00:32:04,100

that can speak to other AI agents. And then number 4, I'd say, which is

503

00:32:04,180 --> 00:32:07,620

the long run, is can your AI with your environmental

504

00:32:08,260 --> 00:32:11,380

automation now be able to talk to, let's say, your ERP systems?

505

00:32:12,160 --> 00:32:14,960

And that's where we kind of move towards this yield prediction

506

00:32:16,000 --> 00:32:19,760

and autonomous growing is when we really first have

507

00:32:19,920 --> 00:32:23,440

thousands of data points. We understand it. The AI has

508

00:32:24,000 --> 00:32:27,280

conversations with you. I mean, all of us are using chat now, right? A year

509

00:32:27,520 --> 00:32:31,360

ago when we were using chat, we didn't trust it. And

510

00:32:31,520 --> 00:32:35,280

now our chat or Claude, whoever you're using, knows

511

00:32:35,440 --> 00:32:39,040

more about you, the way you look at things, the way your

512

00:32:39,840 --> 00:32:43,690

operation operates. Yeah. And now it can advise you. But none of this

513

00:32:43,850 --> 00:32:47,690

stuff can happen overnight. It happens very slowly. And you need

514

00:32:47,690 --> 00:32:50,810

to be able to trust that AI to make decisions on your behalf. So maybe

515

00:32:51,050 --> 00:32:53,930

at first, it's an AI that's going to come back to you and tell you,

516

00:32:54,170 --> 00:32:58,010

hey, I suggest you make this change to your DLI. But you have to have

517

00:32:58,010 --> 00:33:01,290

a set of eyes and say, I trust you, I don't trust you. Or, I'm

518

00:33:01,290 --> 00:33:03,930

just going to do a simulation. I'm just going to do it in an R&D

519

00:33:04,090 --> 00:33:07,690

room and see if this works. So when we talk AI, I think everyone gets

520

00:33:07,850 --> 00:33:10,970

really excited. And we get really excited as a software company. Of course, we get

521

00:33:11,050 --> 00:33:14,450

really excited. We step back and say there's a reality of

522

00:33:14,930 --> 00:33:18,770

how it's going to progress, in my opinion, which is more of an environmental automation

523

00:33:19,010 --> 00:33:22,450

AI that's going to help you versus the yield

524

00:33:22,770 --> 00:33:26,610

predictions. And if you, again, you don't have your data house in order, you

525

00:33:26,610 --> 00:33:29,330

don't have your data, you don't have enough data, or you don't have the data

526

00:33:29,570 --> 00:33:33,410

coming together, how is the AI ever going to scan thousands of data

527

00:33:33,650 --> 00:33:37,170

points to make decisions for you? Yeah, and that's my marketing brain is always on,

528

00:33:37,250 --> 00:33:40,770

as you know, Neta. So it speaks to like this idea of like, Auditing

529

00:33:41,090 --> 00:33:44,850

or having an audit saying, how data ready are you? How data rich are you?

530

00:33:45,010 --> 00:33:48,290

Something along those lines. Because when you talk about these thousands of data points, and

531

00:33:48,290 --> 00:33:52,130

I'm sure Gretchen can speak to this, like, you probably have some folks run

532

00:33:52,290 --> 00:33:55,250

the gamut of just like, oh yeah, I totally get what you say, or like

533

00:33:55,650 --> 00:33:59,490

eyes wide open, like, I'm barely measuring one thing here. The

534

00:33:59,650 --> 00:34:03,410

thought of measuring thousands is just overwhelming. So just one quick

535

00:34:03,490 --> 00:34:07,020

follow-up for Neda is just with the influx of like awareness

536

00:34:07,340 --> 00:34:10,540

around AI, Claude connectors. You know, I myself am like deep in like Claude code.

537

00:34:10,780 --> 00:34:14,380

And so every— if you follow X enough, there's always like Claude for this, Claude

538

00:34:14,460 --> 00:34:17,900

for that. So are you thinking as a company about making connectors,

539

00:34:18,060 --> 00:34:21,660

MCPs? You know, not to get too geeky here, but like stuff that can plug

540

00:34:22,060 --> 00:34:25,900

into Claude easily for folks that are already dabbling? Because I imagine a lot

541

00:34:25,900 --> 00:34:29,740

of these, you know, smaller shops are seeing how they can do more with less,

542

00:34:29,820 --> 00:34:33,610

and then naturally they're leaning into AI. I myself like I've had it build spreadsheets

543

00:34:33,690 --> 00:34:36,730

for me. I've had it connect to my CRM and populate it automatically.

544

00:34:37,370 --> 00:34:40,890

It's my first go-to now. This thing that I used to do manually,

545

00:34:41,370 --> 00:34:44,810

can I automate it? So I'm literally got Claude in a second window, a second

546

00:34:44,970 --> 00:34:48,730

monitor, always seeing how I can put it to work. And I'm curious how

547

00:34:48,810 --> 00:34:52,570

you think about that. Yeah, 100%. I mean, at the core,

548

00:34:52,810 --> 00:34:56,650

you know this about us, Ari, is that we are an integration company. We're

549

00:34:56,650 --> 00:35:00,420

an open company. core, the philosophy of Microclimates

550

00:35:00,500 --> 00:35:03,940

has always been about not living in silos. So absolutely,

551

00:35:04,740 --> 00:35:08,420

we are definitely looking at ways of— and we're already working on some

552

00:35:08,580 --> 00:35:11,780

things on AI— is how do you get it to work with your existing AI,

553

00:35:12,660 --> 00:35:16,500

your large language models, and then how much information can be

554

00:35:16,580 --> 00:35:20,260

shared back and forth. And there's also the security aspect that always has to be

555

00:35:20,500 --> 00:35:24,340

considered. So that's what our technology team is really focusing on, is the security aspect

556

00:35:24,740 --> 00:35:28,350

and how do you keep that information especially now that you're sharing all of a

557

00:35:28,430 --> 00:35:32,190

sudden environmental data, right? We've always believed that you

558

00:35:32,350 --> 00:35:35,950

own your data. We don't own your data, which is why we're an edge company.

559

00:35:36,430 --> 00:35:39,550

Your data is on-site, on-premise, not in cloud. You own your data.

560

00:35:40,430 --> 00:35:44,030

So we gotta be really thinking hard also about the security aspect of it. And

561

00:35:44,030 --> 00:35:47,870

that's what our technology team is focusing on. And it sounds like, Gretchen, when it

562

00:35:47,870 --> 00:35:51,310

comes to security, that's something that's probably near and dear to a lot of growers.

563

00:35:51,950 --> 00:35:55,330

Well, yeah, and it's near and dear to my heart. My mother is, Actually, like

564

00:35:55,410 --> 00:35:59,010

one of the kind of mavens of IT security from the '80s. So,

565

00:35:59,250 --> 00:36:03,010

she worked for a defense contractor in DC. She raised me to not give

566

00:36:03,090 --> 00:36:06,450

out my data on the internet, not talk to strangers, you know, all that sort

567

00:36:06,450 --> 00:36:09,890

of stuff where it was like, security is paramount, like operational

568

00:36:10,370 --> 00:36:14,210

security for business. When we worked with one of the field demonstrations, Neta, I

569

00:36:14,210 --> 00:36:17,010

think it came up that one of the IT teams was like, whoa, whoa, whoa,

570

00:36:17,410 --> 00:36:20,930

you're gonna have a gateway and you're gonna be needing to connect to the internet?

571

00:36:21,090 --> 00:36:24,480

Like, we've got issues with that. And so, I mean, even just that sort of

572

00:36:24,640 --> 00:36:28,320

stuff, even 3 or 4 years ago, now it's funny for me to think that

573

00:36:28,400 --> 00:36:31,520

we have companies that are almost divulging so much to

574

00:36:32,240 --> 00:36:35,600

companies that aren't even their company. So I might recommend

575

00:36:36,080 --> 00:36:39,760

continuing to be cautious with that phased approach, as well as considering making

576

00:36:40,800 --> 00:36:44,640

enterprise AIs that are owned and managed by your enterprise, and

577

00:36:44,880 --> 00:36:48,640

perhaps not sharing everything with external AIs if you

578

00:36:48,800 --> 00:36:52,440

are able to avoid that. I know that there are potentially with like

579

00:36:52,760 --> 00:36:56,520

microclimates, there's going to be ways for you to have AI recommendations that

580

00:36:56,760 --> 00:37:00,520

are essentially always guaranteed to not be being used

581

00:37:00,840 --> 00:37:04,600

to train other growers, for example. I think that was another concern that comes up,

582

00:37:04,760 --> 00:37:08,120

and I want to make sure that continues to be something, you know, credible companies

583

00:37:08,600 --> 00:37:12,280

do. So a grower I respect once said, I expanded one

584

00:37:12,440 --> 00:37:15,960

acre at a time. And I think that person is still in business

585

00:37:16,360 --> 00:37:20,200

and will probably be a good guide to think of as we all adopt more

586

00:37:20,520 --> 00:37:24,320

automation and adopt more AI. I think everyone should explore their options and

587

00:37:24,400 --> 00:37:27,840

find tech that allows for integration of existing systems, whether that's existing

588

00:37:28,160 --> 00:37:31,360

hardware, existing enterprise software, existing AI

589

00:37:31,760 --> 00:37:35,440

agents. It should be easy to install and it should have a low

590

00:37:35,840 --> 00:37:39,200

subscription cost, which I think is something we haven't talked a lot about. But whether

591

00:37:39,280 --> 00:37:42,960

it's AI or whether it's a suite of monitoring and controls

592

00:37:43,200 --> 00:37:47,040

tools, those all have a cost, ongoing cost these days. It's

593

00:37:47,120 --> 00:37:50,650

rare that you find something that you buy it and it's yours now. So I

594

00:37:50,730 --> 00:37:53,850

think that the controls market is competitive though, so there's a lot of price points.

595

00:37:54,330 --> 00:37:58,170

I'm excited to see that we continue to maybe use those levels of sophistication

596

00:37:58,570 --> 00:38:02,250

to help people find the thing that's like right for them. But for me,

597

00:38:02,490 --> 00:38:06,250

I personally don't use AI yet. I am maybe going to be swept

598

00:38:06,410 --> 00:38:10,010

along in the wave eventually, but as an energy person, for me, I just

599

00:38:10,010 --> 00:38:13,610

don't find it something I want to use yet. But I want to support whatever

600

00:38:13,930 --> 00:38:17,550

systems people use, whether they're an industrial, ag, or commercial business. Because

601

00:38:17,710 --> 00:38:20,750

it's not like, Annette, like you've said, it's not up to me to decide how

602

00:38:20,830 --> 00:38:24,670

someone chooses to grow or how to choose to use their data. So as

603

00:38:24,750 --> 00:38:28,590

we get close to wrapping up the conversation, Gretchen, I'm curious, when you

604

00:38:28,750 --> 00:38:32,430

have conversations with companies in the space who are dipping their toe

605

00:38:32,670 --> 00:38:36,350

in the sensor space or trying to revamp legacy systems

606

00:38:37,470 --> 00:38:41,310

or hearing these conversations about AI and daylight control sensors, and, you know, a lot

607

00:38:41,310 --> 00:38:45,160

of it can start to be overwhelming. So for folks looking to get a

608

00:38:45,240 --> 00:38:48,760

start or get a foothold here, what do you usually recommend? Well, I

609

00:38:49,080 --> 00:38:52,840

recommend finding free training. There's a ton of amazing free training

610

00:38:53,160 --> 00:38:56,680

available from almost a decade or more now of free

611

00:38:56,840 --> 00:39:00,520

webinars, short courses by Glaze, the Advanced CEA

612

00:39:01,000 --> 00:39:04,840

team, the Indoor Ag Science Cafe. You don't have to

613

00:39:04,920 --> 00:39:08,760

go in blind and get sold something by, you know, someone at a

614

00:39:08,840 --> 00:39:12,310

trade show. You should get to know people, build relationships, and find out

615

00:39:12,630 --> 00:39:16,470

like what's been proven in growers like you. So for example,

616

00:39:16,470 --> 00:39:19,750

I've got a grower in California who's trying out root zone heating,

617

00:39:20,390 --> 00:39:24,070

and they grow strawberries, and that's not terribly common yet with strawberries.

618

00:39:24,470 --> 00:39:28,310

And so I think a key piece for, you know, persuading that grower

619

00:39:28,470 --> 00:39:32,070

to try it out was getting utility rebate support,

620

00:39:32,310 --> 00:39:36,150

showing them some academic studies, and making them feel like

621

00:39:36,310 --> 00:39:40,110

they can continue to talk to other growers who do it. So those would

622

00:39:40,190 --> 00:39:43,310

be some of the things I say about building trust. I think that the market,

623

00:39:43,790 --> 00:39:47,470

ultimately, people need time. Sometimes my projects take years

624

00:39:47,710 --> 00:39:51,390

to come to fruition because there's other things that are going on, like a pest

625

00:39:51,630 --> 00:39:55,470

management thing or a delivery distribution thing and

626

00:39:55,710 --> 00:39:59,070

trying to get new uptake agreements. So I see

627

00:39:59,950 --> 00:40:03,790

things in like long years, much like construction. You just have to kind

628

00:40:03,870 --> 00:40:06,270

of not see it as something that's going to turn around in the next 6

629

00:40:06,460 --> 00:40:09,580

8 weeks or something. So, Nada, what do you think? 100%.

630

00:40:10,300 --> 00:40:13,660

I think it's built over time. It's a progression. It doesn't happen

631

00:40:14,140 --> 00:40:17,820

quickly. Yeah, continuous improvement, like kaizen. You know,

632

00:40:18,060 --> 00:40:21,660

I think the growers that stand the test of time don't adopt

633

00:40:22,140 --> 00:40:25,980

big rocket ships and then go to the moon. Most folks

634

00:40:26,140 --> 00:40:29,420

are still on the ground, and someone called it a 7-day farmer.

635

00:40:29,980 --> 00:40:33,620

I liked that phrase where it's like they are— that is what they're doing. And

636

00:40:33,620 --> 00:40:37,380

that's what they're invested in. And it's like potentially this work, this

637

00:40:38,420 --> 00:40:41,540

automation, this environmental visibility that is

638

00:40:42,260 --> 00:40:46,100

amongst their priorities, but it's neither urgent nor the top importance.

639

00:40:46,260 --> 00:40:49,300

So that's why we have to take our time to find when does it become

640

00:40:49,540 --> 00:40:52,900

important. Oh, data centers caused your utility rates to go up by

641

00:40:53,460 --> 00:40:57,140

25% and, you know, demand charges have gone up too. Now it's probably

642

00:40:57,300 --> 00:41:00,930

the time to do an audit and improve lighting. So to that,

643

00:41:01,250 --> 00:41:05,010

Neda, how do you think about conversations with new prospects and

644

00:41:05,090 --> 00:41:08,610

people new to understanding this, new to understanding if this is even something that they

645

00:41:08,770 --> 00:41:12,530

need? How do you usually start those conversations given everything we've talked about

646

00:41:12,610 --> 00:41:16,370

today? New to understanding if they need environmental monitoring or controls in

647

00:41:16,450 --> 00:41:20,130

general? Yeah. Oh yeah, I'd say if you're a new operator

648

00:41:20,850 --> 00:41:24,370

and you're starting out and you have a greenhouse operation or you have a vertical

649

00:41:24,610 --> 00:41:28,290

farm, whatever it may be, at bare, bare, bare minimum, we always say you need

650

00:41:28,290 --> 00:41:32,080

to have some monitoring information. Right? You've got to have— you got to

651

00:41:32,480 --> 00:41:35,600

understand what your crops are actually feeling and what they're experiencing.

652

00:41:36,560 --> 00:41:40,240

And if you have one sensor in a greenhouse, it's just

653

00:41:40,400 --> 00:41:44,240

not enough data point. So I'd say to a new operator, I'd

654

00:41:44,240 --> 00:41:47,600

say at bare minimum, start monitoring. You don't necessarily need to jump from monitoring

655

00:41:48,080 --> 00:41:51,440

all the way to automation, right? The automation is like the ideal place, and then

656

00:41:51,520 --> 00:41:55,360

AI automation and algorithms are the next best place that you want to be

657

00:41:55,440 --> 00:41:58,990

at. But You can do a lot of this work with just having monitoring and

658

00:41:59,070 --> 00:42:02,510

setting timers. You know, we've seen operations that work fine,

659

00:42:03,070 --> 00:42:06,510

and they run for a time period at that level,

660

00:42:06,990 --> 00:42:10,270

but then they do need to move up to more of a control where you

661

00:42:10,350 --> 00:42:14,030

have inputs and outputs. So, you have inputs coming from data, from your sensors

662

00:42:14,190 --> 00:42:17,710

that are going to force the output so that your system becomes smarter and

663

00:42:17,870 --> 00:42:21,630

smarter over time. So, I don't think that you necessarily need to go and purchase

664

00:42:22,480 --> 00:42:25,520

I don't believe, I truly do not believe that you need to start an operation

665

00:42:25,760 --> 00:42:29,440

and invest in a $200,000 climate control system. I don't believe

666

00:42:29,520 --> 00:42:33,360

that. I think that you can start off with a few thousand dollars and just

667

00:42:33,520 --> 00:42:37,360

start monitoring, and then set your controls and automation, scale your

668

00:42:37,680 --> 00:42:40,720

operation, go one zone at a time. You don't need to just all of a

669

00:42:40,720 --> 00:42:44,560

sudden spend $200,000 and every single zone is fully automated. You

670

00:42:44,720 --> 00:42:47,280

can start slow because at the end of the day, you're going to run out

671

00:42:47,280 --> 00:42:50,080

of money and you're going to be out of business. So, it's not a good

672

00:42:50,160 --> 00:42:53,870

way of running a business. Well, like that other grower, he used the first

673

00:42:54,110 --> 00:42:57,310

acre to pay for the next acre. So it's like if you do it all

674

00:42:57,390 --> 00:43:01,070

at once, you've essentially taken all that capital

675

00:43:01,310 --> 00:43:04,910

out of what could be sort of like a green revolving fund where I'm like,

676

00:43:05,070 --> 00:43:08,910

great, I improved zone 1. Now zone 1 is costing me less money to operate.

677

00:43:09,070 --> 00:43:12,830

I now have some savings to apply to zone 2 because becoming a

678

00:43:12,910 --> 00:43:16,350

multinational grower wasn't done millions of acres at a

679

00:43:16,430 --> 00:43:19,640

time. Yeah, that approach of going 1 acre at a time, Gretchen, is interesting.

680

00:43:20,440 --> 00:43:24,120

How should growers think about that? Do they need a dedicated R&D

681

00:43:24,280 --> 00:43:27,640

space for these sorts of tests? Can they do it with a sectioned-off area

682

00:43:28,440 --> 00:43:31,960

of their existing growing space? I'm curious logistically how that would work out.

683

00:43:32,280 --> 00:43:35,320

It's easier for indoor farmers to do things like that, I think, because they often

684

00:43:35,480 --> 00:43:38,840

will have specific control zones that are already very well

685

00:43:39,320 --> 00:43:43,160

separated, have different equipment serving it. Greenhouses often will have to put

686

00:43:43,240 --> 00:43:46,680

up makeshift barriers if they want to set up a lighting zone of control that

687

00:43:47,650 --> 00:43:51,410

Like Cornell creates these, you know, T's where

688

00:43:51,490 --> 00:43:55,170

they have 4 different lighting treatments happening in one zone that might normally have

689

00:43:55,330 --> 00:43:58,610

one treatment. And then you might see that in a commercial area as well, that

690

00:43:58,770 --> 00:44:02,370

they start trying something out and have to build up makeshift walls. But with huge

691

00:44:02,610 --> 00:44:05,810

greenhouses, that's not going to be possible. So I think

692

00:44:06,210 --> 00:44:09,890

that's why a larger greenhouse would try it out on a smaller one first, and

693

00:44:09,970 --> 00:44:13,680

then, for example, implement root zone heating or implement energy monitoring

694

00:44:14,300 --> 00:44:17,840

at whole facility. But even if a grower doesn't have an acre, it's like one

695

00:44:18,080 --> 00:44:21,840

zone at a time, I think, is where it comes to every size grower. One

696

00:44:22,000 --> 00:44:24,960

room at a time. We do that at home, right? We don't renovate our whole

697

00:44:25,040 --> 00:44:28,240

house at once. Generally, that would be extraordinarily disruptive and we'd have no money to

698

00:44:28,240 --> 00:44:32,080

go on vacation or do anything else that's nice. So yeah, I think we

699

00:44:32,160 --> 00:44:35,840

should take it as we all want to continuously improve so that

700

00:44:36,080 --> 00:44:39,680

we are resilient and sustainable. And that doesn't just mean that we feel good about

701

00:44:39,760 --> 00:44:43,160

the environment. It means that we're here to do business next year. So yeah,

702

00:44:43,560 --> 00:44:47,400

I love what Aneta said about what the plants are feeling because— I love

703

00:44:47,480 --> 00:44:51,240

that too. I was like, wow, that's a really good phrase. They're living organisms,

704

00:44:51,560 --> 00:44:55,160

you know, they're living things. And they can't talk, right? So

705

00:44:55,560 --> 00:44:59,320

these systems, they get insight that allows us to not

706

00:44:59,480 --> 00:45:03,240

just save money but to actually have like real, like you

707

00:45:03,320 --> 00:45:07,000

said, like almost organic impacts. They can't talk and you

708

00:45:07,160 --> 00:45:10,930

can add sensors to get them to talk for you. So you can have

709

00:45:11,090 --> 00:45:14,530

leaf temperature. That's how I think of a plant talking back to me, right? Is

710

00:45:15,090 --> 00:45:18,450

if I can't— if you can't talk, I love that what you said, Gretchen. If

711

00:45:18,690 --> 00:45:21,970

a plant can't talk, can you have a leaf temperature sensor

712

00:45:22,770 --> 00:45:25,890

that is going to talk on behalf of the plant and let you know,

713

00:45:26,770 --> 00:45:30,610

this is what I'm feeling? And then can you take that information and

714

00:45:30,690 --> 00:45:34,530

feed into HVAC system because the humidity is too high in the room? And can

715

00:45:34,610 --> 00:45:38,180

you reduce that by 3%? And then it will talk back and say,

716

00:45:38,900 --> 00:45:42,500

I'm feeling better because I am in the threshold that I like to be in.

717

00:45:43,140 --> 00:45:46,020

Yeah, they will get to the point where the AI is actually speaking for the

718

00:45:46,100 --> 00:45:48,900

plants. I think about those experiments when I was in grade school. They would play

719

00:45:49,380 --> 00:45:52,820

classical music for one set of plants and heavy metal for the others, and the

720

00:45:52,900 --> 00:45:56,580

classical music plants would do better. So is anyone doing tests with music

721

00:45:56,820 --> 00:46:00,340

in greenhouses? I can't speak for that, but I bet you a lot of the

722

00:46:00,500 --> 00:46:04,020

researchers that I work with talk to their plants because they have shown that does

723

00:46:04,340 --> 00:46:08,170

result in better outcomes. For sure. I will admit that I do.

724

00:46:08,570 --> 00:46:11,930

I will admit that I have a banana tree in my yard right now, and

725

00:46:12,090 --> 00:46:15,930

for the first time in Seattle, it's actually flowered 3 different flowers.

726

00:46:16,410 --> 00:46:20,250

Thank you. And we're not supposed to grow bananas in Seattle, but I have been

727

00:46:20,250 --> 00:46:23,370

talking to my banana tree. I'm sure it's helping.

728

00:46:24,170 --> 00:46:27,130

So Gretchen, I'll go with you first and then Neta, just closing thoughts on this

729

00:46:27,450 --> 00:46:30,810

conversation, where we are, or maybe some thoughts about the kind of the space as

730

00:46:30,810 --> 00:46:34,410

a whole. We did mention Henry posting, you know, status of

731

00:46:34,410 --> 00:46:38,250

what's happening in CEA. So I'm curious your 2 cents on what you see

732

00:46:38,330 --> 00:46:41,930

from your perspective. Well, at the beginning of the year, I predicted a year of

733

00:46:42,010 --> 00:46:45,770

retrofits, and I think that is what we see

734

00:46:45,930 --> 00:46:49,610

happening and consolidation. And that doesn't necessarily mean,

735

00:46:50,090 --> 00:46:53,930

you know, rocky situation for everyone. Ultimately, for Neta

736

00:46:54,090 --> 00:46:57,130

and I, it means that we can work with people to, like, I think in

737

00:46:57,130 --> 00:47:00,730

Henry's post, he said like a fairly well-built greenhouse is a very, very valuable

738

00:47:01,050 --> 00:47:04,860

asset always, regardless of what happened to the company that owned it. So,

739

00:47:05,420 --> 00:47:08,300

you know, times may change and the owners may shift, but we're there to help

740

00:47:08,460 --> 00:47:11,580

that greenhouse become better. And so I think that is going to be an opportunity

741

00:47:11,900 --> 00:47:15,580

we continue to do more as the vertical farms as well. How

742

00:47:15,740 --> 00:47:19,580

can we get more monitoring and help that overhead costs go down so that whoever

743

00:47:19,820 --> 00:47:23,500

takes that asset is going to have overall an asset that

744

00:47:23,660 --> 00:47:27,500

is very profitable? And I also, for the rest of the

745

00:47:27,500 --> 00:47:31,320

year, I see For my point, I'm going to be at GreenTech Philly. I'll be

746

00:47:31,320 --> 00:47:35,160

doing a talk on the opportunities that are being presented by new

747

00:47:35,480 --> 00:47:39,160

energy regulations and how that might allow for growers

748

00:47:39,320 --> 00:47:43,000

to start exchanging energy with, you know, unique type of buildings like data

749

00:47:43,240 --> 00:47:47,000

centers and others. So that could be a cool talk. And overall,

750

00:47:47,160 --> 00:47:50,440

I would hope to share soon the results of the NYSERDA research that all the

751

00:47:50,520 --> 00:47:53,960

glaze researchers worked on for the past 10 years. So that's going to be pretty

752

00:47:54,120 --> 00:47:56,520

exciting. Those are kind of the 2 things for the rest of my year. Okay.

753

00:47:56,490 --> 00:47:59,770

Thank you. Nada, what's on your radar? Yeah, it's, you know,

754

00:48:00,170 --> 00:48:03,450

Gretchen mentioned the beginning of the year, her prediction. I'd say for the past

755

00:48:04,250 --> 00:48:07,850

2 years, our prediction has been that we're going to— integration is going to be

756

00:48:08,570 --> 00:48:12,410

the buzzword, and we're beginning to see more and more of that. Certainly, it

757

00:48:12,490 --> 00:48:15,610

was a buzzword at Indoor@Con, and there was a panelist

758

00:48:17,850 --> 00:48:21,370

discussion with our CTO that was involved that talked about what does it mean to

759

00:48:21,450 --> 00:48:25,230

be integrated, Why is that so important? So, I think integration is going to

760

00:48:25,230 --> 00:48:28,590

be the ongoing theme for a long time ahead. It's just

761

00:48:29,070 --> 00:48:32,190

necessary. So, I think that's going to continue happening, and it's already happening for us.

762

00:48:32,190 --> 00:48:35,630

We're going to go down that path even further. We're beginning to see more and

763

00:48:35,630 --> 00:48:39,390

more companies that have the more legacy control systems changing

764

00:48:39,790 --> 00:48:43,310

their models even and opening up their APIs and making it a lot more accessible

765

00:48:43,790 --> 00:48:46,910

for companies to integrate with them. So, I think that thing is going to

766

00:48:47,150 --> 00:48:50,990

continue. The other prediction that we've had for the past couple of years, and we're

767

00:48:51,070 --> 00:48:54,310

just now starting to get there, is that these LoRaWAN wireless

768

00:48:54,710 --> 00:48:58,550

sensors are going to take off in this industry. They've taken off in

769

00:48:58,630 --> 00:49:02,470

other industries, but in this industry, it's sort of been, we've had some systems, you

770

00:49:02,470 --> 00:49:06,150

know, you've got the Aranet systems, the Arroyo system, you've had other systems in the

771

00:49:06,150 --> 00:49:09,030

market, but it's really gonna be about these open platforms

772

00:49:09,990 --> 00:49:13,590

that the customer has freedom of choice. And we're hearing this over and over from

773

00:49:13,750 --> 00:49:17,350

customers, and they get really excited when they take a look at our website and

774

00:49:17,510 --> 00:49:21,170

we have 10 different sensors you can choose from, temperature humidity

775

00:49:21,330 --> 00:49:24,690

sensors. You don't have to— you can pick and choose from 10 different vendors, 10

776

00:49:24,770 --> 00:49:28,450

different manufacturers. So I think that theme is also going to continue,

777

00:49:28,690 --> 00:49:32,370

that there's an excitement for these customers and operators to have

778

00:49:32,530 --> 00:49:36,130

the ability to pick and choose what's best for their operation

779

00:49:36,610 --> 00:49:40,050

and change vendors if they need to because a new

780

00:49:40,770 --> 00:49:43,730

product has hit the market. So we've done a lot of integrations this year. It's

781

00:49:43,810 --> 00:49:47,350

been really exciting, new integrations this year. Well, I appreciate that

782

00:49:47,590 --> 00:49:49,830

feedback from you both because it seems like you both have a finger on the

783

00:49:49,830 --> 00:49:53,590

pulse in your respective spaces about what's happening, where things are headed, because you're on

784

00:49:53,590 --> 00:49:56,870

the ground and you're doing the work, working with growers. And I love to see

785

00:49:56,950 --> 00:50:00,070

these types of partnerships, and I'm sure there's a lot more happening. So if there's

786

00:50:00,070 --> 00:50:02,870

others that I'm not aware of and you need to bring them to my attention,

787

00:50:03,110 --> 00:50:05,590

we'll get them on the show as well. But, you know, to see you guys

788

00:50:05,670 --> 00:50:08,550

working together is really exciting because you're both bringing your respective

789

00:50:09,430 --> 00:50:13,190

specialties and strengths, and it's so It feels like a 1 1 3

790

00:50:13,670 --> 00:50:17,110

result here. So I appreciate the work both of you are doing for this space.

791

00:50:17,670 --> 00:50:20,310

So Gretchen, best place for folks to connect with you if they want to learn

792

00:50:20,390 --> 00:50:23,830

more? I'm on LinkedIn, Gretchen Schimmelfennig, and

793

00:50:24,230 --> 00:50:25,990

other social media. I have other lives.

794

00:50:28,550 --> 00:50:32,390

And Neta? Same here, on LinkedIn. And you can always go to microclimates.com

795

00:50:32,470 --> 00:50:35,270

and schedule a meeting with me directly. Okay. We'll make sure all those links are

796

00:50:35,270 --> 00:50:38,870

in the show notes. Thank you both again for an engaging conversation. Thank you. Thanks

797

00:50:39,030 --> 00:50:39,750

so much, Ari.