1 00:00:02,160 --> 00:00:02,700 Kaisha: All right. 2 00:00:02,700 --> 00:00:04,620 It's Thursday at 4:20 PM. 3 00:00:04,650 --> 00:00:05,100 Eastern. 4 00:00:05,100 --> 00:00:08,010 That means it's time for office hours, a Roy's weekly session for 5 00:00:08,010 --> 00:00:11,700 cultivators, so that you can hear from experts and talk to each other about 6 00:00:11,700 --> 00:00:13,140 what they're seeing with their grows. 7 00:00:13,380 --> 00:00:14,139 My name is Kaisha. 8 00:00:14,160 --> 00:00:18,090 I'll be moderating solo today while my co may moderating Mandy's on vacation. 9 00:00:18,095 --> 00:00:18,840 We miss you Mandy. 10 00:00:18,878 --> 00:00:22,473 But looking forward to her coming back soon as always, if you are live with 11 00:00:22,473 --> 00:00:25,833 us, have any questions, you can feel free to type it in the chat at any time. 12 00:00:25,833 --> 00:00:26,553 And if your questions. 13 00:00:26,873 --> 00:00:30,023 Picked we'll have you either unmute yourself or I can ask for you. 14 00:00:30,293 --> 00:00:31,553 We're also fielding questions from you. 15 00:00:31,553 --> 00:00:34,403 Two live, welcome to everybody out there, and you're welcome 16 00:00:34,403 --> 00:00:35,513 to post your questions there. 17 00:00:35,518 --> 00:00:37,793 Don't forget to like, and subscribe while you are there. 18 00:00:38,063 --> 00:00:41,033 First time question askers, get swag, everyone on today, we'll have 19 00:00:41,033 --> 00:00:44,483 a chance to win a limited edition around a t-shirt just like mine. 20 00:00:44,753 --> 00:00:46,553 Just type in an email address into the chat. 21 00:00:46,593 --> 00:00:48,923 And that will enter you into the raffle. 22 00:00:49,283 --> 00:00:50,213 Seth and Jason. 23 00:00:50,483 --> 00:00:50,903 How's it going? 24 00:00:52,128 --> 00:00:53,448 Jason: Doing well, good 25 00:00:53,538 --> 00:00:53,808 Kaisha: vacation. 26 00:00:54,288 --> 00:00:55,278 Nice to see you guys. 27 00:00:55,278 --> 00:00:56,778 We've been a lot of vacations lately. 28 00:00:56,778 --> 00:00:56,958 Nice. 29 00:00:56,958 --> 00:00:59,028 See you both in the same place at the same time. 30 00:01:00,528 --> 00:01:00,828 yeah. 31 00:01:00,828 --> 00:01:04,373 So, we're going to, we're a little light on social media questions this week. 32 00:01:04,373 --> 00:01:07,373 And so it's a great opportunity to do a little deep dive on something you're 33 00:01:07,373 --> 00:01:11,578 saying maybe with our clients, with what's what's going on with with that today. 34 00:01:11,578 --> 00:01:12,748 What do you guys got going on over there? 35 00:01:13,438 --> 00:01:16,209 Jason: So we're gonna talk a little bit about harvest group analytics. 36 00:01:16,214 --> 00:01:20,646 So when when you're wrapped up with your cycle, What you can what you can 37 00:01:20,646 --> 00:01:24,666 see, what you can look at document analyze to get a better idea of how 38 00:01:24,666 --> 00:01:28,566 well that cycle performed based on the, the data that you've been capturing. 39 00:01:30,696 --> 00:01:31,656 Seth: Yeah, absolutely. 40 00:01:31,661 --> 00:01:34,806 I mean, you know, Roy is not just about monitoring your day to day 41 00:01:34,806 --> 00:01:38,316 functionality and your grow a big part of it is logging that. 42 00:01:38,316 --> 00:01:40,446 So we can look back at the end of around and look at, you 43 00:01:40,446 --> 00:01:42,306 know, holistic crop performance. 44 00:01:42,306 --> 00:01:43,296 Where did we mess up? 45 00:01:43,296 --> 00:01:44,796 What were some of the challenges we had. 46 00:01:45,641 --> 00:01:48,971 And, you know, what, what can we actually pinpoint in this whole time period 47 00:01:48,971 --> 00:01:52,391 that might help us look at, you know, where we can make some improvements. 48 00:01:53,741 --> 00:01:56,411 It's, it's really important to look back at a whole run, not just 49 00:01:56,411 --> 00:01:58,361 focus on your day to day decisions. 50 00:01:59,831 --> 00:02:02,411 Jason: So without further ado, I'll share my screen here. 51 00:02:02,411 --> 00:02:07,479 I've got got our interface going and we will talk to you guys about what 52 00:02:07,529 --> 00:02:09,239 what kind of features here are in. 53 00:02:09,989 --> 00:02:10,919 The analytics page. 54 00:02:11,039 --> 00:02:14,459 So kind of just to get started, there's a few ways to get 55 00:02:14,459 --> 00:02:16,259 to harvest group analytics. 56 00:02:16,259 --> 00:02:20,459 If we jump into our production, we can go into the analytics of, of 57 00:02:20,459 --> 00:02:23,919 currently growing ones for talking about all the features here, we're 58 00:02:23,919 --> 00:02:27,324 gonna go into some that are finished up and take a look at the data that 59 00:02:27,324 --> 00:02:29,724 we summarize for those harvest groups. 60 00:02:30,504 --> 00:02:32,683 So let's just jump into some banana O. 61 00:02:34,678 --> 00:02:38,848 Looks like we've got pineapple express and Jenny's test in here. 62 00:02:38,848 --> 00:02:44,006 So one of the most important things to get harvest group analytics, when you're 63 00:02:44,006 --> 00:02:48,866 building your harvest group, make sure that you've got the cultivars outlined 64 00:02:48,866 --> 00:02:51,026 on the benches or the, the zones. 65 00:02:51,661 --> 00:02:54,781 So when we look at this flower room, we can see our banana OGs in zone 66 00:02:54,781 --> 00:02:59,071 one, by now express the zone two in Jenny's test zone three and four. 67 00:02:59,371 --> 00:03:03,781 This is what's going to populate your substrate data 68 00:03:04,021 --> 00:03:06,331 into the specific cultivars. 69 00:03:06,331 --> 00:03:11,431 And what we're doing that for is just to make sure that we can separate any of 70 00:03:11,431 --> 00:03:15,301 the, the performance that we might see in, in the different genetics in the room. 71 00:03:15,541 --> 00:03:18,601 So make sure that we get those all populated when we're building our harvest. 72 00:03:19,386 --> 00:03:23,226 One of the easiest ways to know that you haven't done that is you may or 73 00:03:23,226 --> 00:03:27,844 may not see water content and EC data right here in our environment tab. 74 00:03:27,849 --> 00:03:31,737 If if we didn't populate what zones or detail, what zones 75 00:03:31,737 --> 00:03:32,877 those cold fires are running. 76 00:03:33,507 --> 00:03:37,767 So for today's topic, let's jump into this analytics page and we'll take 77 00:03:37,767 --> 00:03:43,827 a look and see what information is provided after we've done the grow cycle. 78 00:03:44,522 --> 00:03:47,822 So if we just start off at the top, it gives us a little bit of a summary 79 00:03:47,822 --> 00:03:52,112 of our recipe talking about how long we ran each of the stages of. 80 00:03:54,512 --> 00:04:00,692 And then if we go down here, we can see our, our wet weights and our dry weights. 81 00:04:00,812 --> 00:04:03,992 And we can go jump in just to the, the yield analytics, which 82 00:04:03,992 --> 00:04:06,482 we'll see down here at the bottom of this page in just a minute. 83 00:04:06,932 --> 00:04:08,492 And then there's also the gallery, which. 84 00:04:09,107 --> 00:04:13,337 Looks like someone's been putting some stock photography in this harvest script, 85 00:04:13,337 --> 00:04:16,787 not, not the best representative of, of what our plants look like for that. 86 00:04:16,787 --> 00:04:21,192 But yeah, this is obviously in our, our demo facility where we do a little bit 87 00:04:21,192 --> 00:04:23,412 of plan around on the software side. 88 00:04:23,472 --> 00:04:27,972 So when we're looking at this cultivation schedule, we can break it down and 89 00:04:28,242 --> 00:04:32,973 get an idea of how many tasks and when these tasks are getting done. 90 00:04:32,973 --> 00:04:35,433 So this is kind of a good way you can visually see. 91 00:04:36,448 --> 00:04:39,488 How much, you know, labor is going on specific days. 92 00:04:39,488 --> 00:04:44,491 Hopefully y'all are using the tasking feature for any of the transplanting, 93 00:04:44,491 --> 00:04:49,559 any of the IPM events, any pruning type of strategies, tagging strategies, 94 00:04:49,829 --> 00:04:53,700 really, whatever that whatever you wanna benchmark as a point in 95 00:04:53,700 --> 00:04:55,333 time that work needs to get done. 96 00:04:56,173 --> 00:04:57,733 The next up is the alert. 97 00:04:57,733 --> 00:05:01,093 So this alerts overview is a really nice way to think about. 98 00:05:02,248 --> 00:05:06,538 When you do struggle to stay within the parameters of your environment 99 00:05:06,598 --> 00:05:08,188 and or irrigation behaviors. 100 00:05:08,308 --> 00:05:12,838 So, you know, maybe when, you know, after we do a pruning, it's hard for us to keep 101 00:05:12,838 --> 00:05:14,843 our humidity high enough in the room. 102 00:05:14,890 --> 00:05:16,390 Any of those types of things. 103 00:05:16,390 --> 00:05:18,940 And obviously when you're looking from harvest group to harvest 104 00:05:18,940 --> 00:05:22,960 groups, it's nice to, to recognize any specific points in time that you 105 00:05:22,960 --> 00:05:25,282 do typically see alerts go off and. 106 00:05:25,937 --> 00:05:29,987 That that'll kind of give you an idea of when we need to dig in and 107 00:05:29,987 --> 00:05:32,315 understand what what we can improve upon. 108 00:05:32,705 --> 00:05:35,405 Obviously this one doesn't have any alerts, so that's really nice 109 00:05:35,410 --> 00:05:39,485 to see we made it the whole cycle without getting an emergency text 110 00:05:39,490 --> 00:05:40,805 message telling us we're out range. 111 00:05:41,935 --> 00:05:42,465 The sticks. 112 00:05:42,465 --> 00:05:46,639 One said the target ranges and here we have EC display. 113 00:05:46,732 --> 00:05:50,542 You can go ahead and select any of the parameters, the data parameters that are 114 00:05:50,542 --> 00:05:55,642 being captured from the AOR system there and, and take a look and see how close 115 00:05:55,642 --> 00:05:58,132 to those target ranges that you state. 116 00:05:58,617 --> 00:05:59,577 Throughout that harvest group. 117 00:05:59,577 --> 00:06:04,647 So the yellow box in this case and the outside is gonna be the target ranges. 118 00:06:04,707 --> 00:06:08,907 We can see that adjust throughout the different phases of the grow cycle. 119 00:06:09,177 --> 00:06:14,547 And then we can see the average for the room is the, the data line in the middle. 120 00:06:14,907 --> 00:06:17,127 And this one, we did a pretty good job towards the end. 121 00:06:17,127 --> 00:06:20,787 We looks like we forgot to drop the EC, like we had intended to 122 00:06:20,787 --> 00:06:23,217 do to satisfy our recipe targets. 123 00:06:24,337 --> 00:06:26,857 And then one of the most important things here as well is it tells us how 124 00:06:26,857 --> 00:06:28,957 long we were outside of that range. 125 00:06:28,957 --> 00:06:32,317 So in this case, our UC spent 15 days outside a range, right? 126 00:06:32,977 --> 00:06:37,822 If we jump into something like air temperature, This is 64 days. 127 00:06:37,822 --> 00:06:40,372 So we, we really struggled with our, our air temp. 128 00:06:40,379 --> 00:06:43,829 On this one, we can see, obviously our, our target ranges were quite a 129 00:06:43,829 --> 00:06:48,419 bit tighter than we could actually run with the equipment in that room. 130 00:06:48,599 --> 00:06:52,066 And so that's, what's going collaborate to that that time outside of range. 131 00:06:52,456 --> 00:06:55,576 Ideally, you're gonna have zero time outside of your target ranges. 132 00:06:55,576 --> 00:06:58,216 And I guess this is a great time to talk about the difference 133 00:06:58,216 --> 00:06:59,836 between alerts and target ranges. 134 00:06:59,836 --> 00:07:02,073 So, target range is the ideal. 135 00:07:02,478 --> 00:07:05,718 Parameter that we're trying to keep any of our variables within. 136 00:07:05,778 --> 00:07:10,758 So that's like the golden standard, whereas an alert range is, needs to be 137 00:07:10,763 --> 00:07:14,131 set with a little bit of bandwidth so that you know, if, if something's just a 138 00:07:14,131 --> 00:07:18,271 little bit screwy, you're not getting text messages, but if equipment failure happens 139 00:07:18,276 --> 00:07:21,691 or someone does a set point incorrectly, then you'll definitely get notified. 140 00:07:22,546 --> 00:07:22,846 Seth: Yeah. 141 00:07:22,846 --> 00:07:25,756 And, and that's honestly too, where when you're setting up those target ranges, it 142 00:07:25,756 --> 00:07:29,656 is best practice, honestly, to have, if you can two screens, but two tabs open. 143 00:07:30,106 --> 00:07:32,986 So you can kind of go back and look at your dashboard and start establish, 144 00:07:33,076 --> 00:07:34,936 establishing realistic parameters. 145 00:07:34,936 --> 00:07:37,996 You know, if I go ahead and say, all right, analytically, I want to 146 00:07:37,996 --> 00:07:40,786 keep 80 to 82 in early flower, but. 147 00:07:41,821 --> 00:07:44,341 My chart looks a little more like the one Jason's displaying here. 148 00:07:44,671 --> 00:07:47,731 I might go ahead and say, all right, I'm definitely gonna set that alert range. 149 00:07:47,731 --> 00:07:51,121 Well, outside of my analytical range, because I do not want 150 00:07:51,121 --> 00:07:53,101 to start ignoring those alerts. 151 00:07:54,541 --> 00:07:54,811 Jason: Yeah. 152 00:07:54,841 --> 00:07:58,741 And that that's kind of the best way to make sure your alert, target ranges are 153 00:07:58,741 --> 00:08:03,338 tailored correctly is go back and look at the harvest group that you didn't have any 154 00:08:03,338 --> 00:08:05,678 equipment failures or any mistakes happen. 155 00:08:05,683 --> 00:08:09,428 And, and look at what you can typically achieve when, when you are meeting 156 00:08:09,428 --> 00:08:10,968 the, the ranges that you like to. 157 00:08:12,443 --> 00:08:13,223 Seth: Yeah, absolutely. 158 00:08:13,223 --> 00:08:16,153 And honestly, I think that it's a great way to go back and look at your data 159 00:08:16,153 --> 00:08:19,453 for any one parameter over the whole run and identify, especially with the 160 00:08:19,453 --> 00:08:20,810 time counter, you know, a big issue. 161 00:08:20,810 --> 00:08:23,980 I find a lot of people running into is let's say water content in rockwool. 162 00:08:24,455 --> 00:08:26,435 It's like, man, I am just struggling with these yields. 163 00:08:26,435 --> 00:08:29,975 I can't get above like two pounds of light and we'll go back and look 164 00:08:29,975 --> 00:08:33,125 and say, okay, your water content's been relatively low the whole time. 165 00:08:33,125 --> 00:08:36,215 That's one of the key fundamental things that a plant needs to grow. 166 00:08:36,215 --> 00:08:40,355 If we've limited that over, you know, if we've spent 64 days below our 167 00:08:40,355 --> 00:08:43,235 water content goals, for instance, we can start to expect to see a pretty 168 00:08:43,235 --> 00:08:44,945 quantifiable reduction in yield. 169 00:08:45,575 --> 00:08:49,925 So we can actually look, you know, backward in time and start to quantify 170 00:08:50,285 --> 00:08:53,405 how much we might expect to gain from making certain adjustments. 171 00:08:54,125 --> 00:08:57,575 Especially if we have the data of a good run versus a bad run to compare 172 00:08:57,575 --> 00:09:01,925 it to, we can really parse out like, okay, we've eliminated most of the 173 00:09:01,925 --> 00:09:06,725 variables that time outside range for EC or water content seems to be the 174 00:09:06,725 --> 00:09:07,925 biggest variable we're working with. 175 00:09:07,925 --> 00:09:09,215 Let's attack that next. 176 00:09:11,090 --> 00:09:11,810 Jason: Exactly. 177 00:09:12,170 --> 00:09:14,540 And we'll just move into the next section here. 178 00:09:14,540 --> 00:09:19,036 So, plant development, looking at the entire cycle, this is where we're 179 00:09:19,096 --> 00:09:20,596 displaying some of the manual readings. 180 00:09:20,656 --> 00:09:24,316 And obviously if you've listened to this show very much, we talk 181 00:09:24,321 --> 00:09:28,396 about canopy height quite a bit when indicating how long we should be 182 00:09:28,396 --> 00:09:29,986 running different steering techniques. 183 00:09:30,486 --> 00:09:30,836 So. 184 00:09:31,126 --> 00:09:34,486 This is a great way to just kind of visualize what happened as 185 00:09:34,486 --> 00:09:36,046 far as those manual readings go. 186 00:09:36,376 --> 00:09:37,876 This case, we can see our, our canopy height. 187 00:09:39,451 --> 00:09:44,641 Definitely weighed off right there about, you know, looks like April 29th. 188 00:09:45,151 --> 00:09:46,981 It kind of started slowing down. 189 00:09:46,981 --> 00:09:49,561 And that's usually when we'll flip back to vegetative bulking. 190 00:09:49,711 --> 00:09:53,990 So, notes, pacing, another good good metric to take in there and then stem 191 00:09:53,990 --> 00:09:57,944 diameter kind of just a good idea to keep track of that and see what it looks 192 00:09:57,944 --> 00:10:02,174 like in relationship to the, the steering parameters that you're pushing onto that. 193 00:10:05,249 --> 00:10:07,269 And then our past, go ahead, Kaisha. 194 00:10:07,439 --> 00:10:08,639 Kaisha: I was actually, this is amazing. 195 00:10:08,639 --> 00:10:11,369 I just wanna, I'm just applying this to my mind. 196 00:10:11,369 --> 00:10:14,459 I'm a consumer, mostly, I've got two little babies in the back. 197 00:10:14,459 --> 00:10:17,609 I'm trying to grow, but I'm nowhere near anybody else's level on here, but 198 00:10:17,609 --> 00:10:18,809 I'm just wrapping my head around this. 199 00:10:18,809 --> 00:10:22,319 So if I'm a cultivator, I have like an award-winning cultivar. 200 00:10:22,499 --> 00:10:23,789 It's my number one seller. 201 00:10:23,789 --> 00:10:27,659 And I wanna make sure I wanna ensure that it is grown properly 202 00:10:27,989 --> 00:10:29,429 for the consistency, the. 203 00:10:29,784 --> 00:10:31,764 Reliable potency every single time. 204 00:10:31,974 --> 00:10:34,721 These analytics are really what I'm looking at to determine what 205 00:10:34,721 --> 00:10:37,571 works and what I can, what I need to be doing, going forward. 206 00:10:37,571 --> 00:10:39,191 What I need to adjust going forward is that. 207 00:10:40,276 --> 00:10:41,266 Jason: Yeah, absolutely. 208 00:10:41,266 --> 00:10:43,996 And when we talk about things like manual readings, there are 209 00:10:44,066 --> 00:10:47,576 some really crucial points of time when you should be capturing that. 210 00:10:47,576 --> 00:10:51,176 So when you're coming out of veg, definitely make sure you've 211 00:10:51,176 --> 00:10:52,586 got your, your plant height. 212 00:10:52,676 --> 00:10:56,126 And that's one of the indicators where we know, Hey, we're on our, our bed 213 00:10:56,126 --> 00:11:00,308 schedule to Hit the appropriate size plant that we need for our yields or, or 214 00:11:00,308 --> 00:11:02,138 maybe you're behind it or ahead of it. 215 00:11:02,228 --> 00:11:06,680 And you can make adjustments to stay within projectability of 216 00:11:06,680 --> 00:11:07,910 your, your crop yields coming 217 00:11:07,910 --> 00:11:08,330 Seth: out of there. 218 00:11:09,320 --> 00:11:09,980 Oh, absolutely. 219 00:11:09,980 --> 00:11:12,560 And we can also start to look at, like, for instance, if your canopy 220 00:11:12,565 --> 00:11:15,695 height, one run to the next was 14 inches or centimeters taller 221 00:11:15,695 --> 00:11:17,135 yet your yield was the same. 222 00:11:17,285 --> 00:11:18,215 Then we can start dialing. 223 00:11:18,215 --> 00:11:18,425 Okay. 224 00:11:18,425 --> 00:11:19,955 We can probably shorten up our veg time then. 225 00:11:20,225 --> 00:11:20,645 Sure. 226 00:11:20,705 --> 00:11:21,695 We can go with a smaller plant. 227 00:11:21,695 --> 00:11:24,395 I mean, we, we have a lot of options to look back on, and this is a good 228 00:11:24,395 --> 00:11:25,955 way to evaluate that performance. 229 00:11:25,955 --> 00:11:30,635 Because again, we say it all the time, everything is so cyclical, you 230 00:11:30,635 --> 00:11:33,325 know, at pretty much any cannabis growth facility and you're growing 231 00:11:33,425 --> 00:11:35,015 the same strains over and over. 232 00:11:35,345 --> 00:11:38,915 It just gets to be a lot of really specific information to keep track of. 233 00:11:39,245 --> 00:11:42,545 And if you've got an easy way to organize it, you can quickly go back and look and. 234 00:11:43,235 --> 00:11:45,995 Validate your decisions or decide you're gonna make a different one. 235 00:11:48,125 --> 00:11:49,025 Jason: Exactly. 236 00:11:49,115 --> 00:11:51,105 Next up just IPM application. 237 00:11:51,135 --> 00:11:55,095 It's just charting when those were happening over the, over the growth cycle. 238 00:11:55,155 --> 00:11:56,938 So, looks like two more than usual. 239 00:11:57,418 --> 00:11:59,338 Maybe we had a little bit higher pest pressure. 240 00:11:59,367 --> 00:12:02,686 During this, this cycle, we also look at waste amounts. 241 00:12:02,776 --> 00:12:06,266 So if you are metric integrated, you're tracking the, the waste 242 00:12:06,266 --> 00:12:07,222 amounts from those plants. 243 00:12:07,222 --> 00:12:08,182 So we can note when that. 244 00:12:08,226 --> 00:12:11,466 Mixed up in this analytics, just sensor readings great way to look 245 00:12:11,466 --> 00:12:15,230 at the specific sensor spread of any of those sensor types. 246 00:12:15,350 --> 00:12:20,270 So nice way to just break out a, a harvest group and then take a look at anything 247 00:12:20,270 --> 00:12:24,503 specifically that you'd like to take a look at, and we can also zoom into a. 248 00:12:25,598 --> 00:12:26,348 These parameters. 249 00:12:26,348 --> 00:12:30,008 And what we'll notice is we've got a solid line in the middle, which 250 00:12:30,008 --> 00:12:33,510 is gonna be the room average for the sensors that are getting displayed. 251 00:12:33,630 --> 00:12:38,370 And then we've got kind of a shaded band talking about the, 252 00:12:38,370 --> 00:12:42,300 the variation from, from low to, to highest sensor data points. 253 00:12:44,670 --> 00:12:44,850 Seth: Yep. 254 00:12:44,850 --> 00:12:48,150 So over time for any specific cultivar we're growing, we wanna see 255 00:12:48,150 --> 00:12:49,860 that shaded van shrink and shrink. 256 00:12:49,860 --> 00:12:51,810 We want our crop to get more and more consistent. 257 00:12:51,810 --> 00:12:56,670 If, and I mean, obviously you could look at this in numbers as well, but the 258 00:12:56,670 --> 00:13:00,210 shaded highlighter line is a really nice way to just get a quick visual snapshot 259 00:13:00,210 --> 00:13:02,100 at how consistent your rooms are being. 260 00:13:02,105 --> 00:13:04,290 Especially if you wanna go back over time and say, okay, 261 00:13:04,290 --> 00:13:05,340 here's my first run with Arro. 262 00:13:06,735 --> 00:13:08,445 We had a pretty wide confidence band there. 263 00:13:08,775 --> 00:13:10,575 And then, Hey, look, we can quantify each runt. 264 00:13:10,575 --> 00:13:14,565 We're getting it that much closer to being very, you know, perfectly consistent 265 00:13:14,565 --> 00:13:16,155 and nothing's ever gonna be perfect. 266 00:13:16,815 --> 00:13:19,815 you're never, if we zoomed in far enough, there would still be that shaded 267 00:13:19,815 --> 00:13:22,523 line there outside of the solid line. 268 00:13:22,528 --> 00:13:22,703 But. 269 00:13:23,318 --> 00:13:24,368 Again, quick glance. 270 00:13:24,428 --> 00:13:27,908 You can really start to tone in on, okay, what am I struggling 271 00:13:27,908 --> 00:13:29,018 the most with in this room? 272 00:13:29,023 --> 00:13:30,758 Cuz sometimes it's not always what people think. 273 00:13:31,718 --> 00:13:32,108 You know? 274 00:13:32,168 --> 00:13:34,748 I mean, sometimes you've got a lot of things nailed down and you're like, 275 00:13:34,748 --> 00:13:36,638 wow, my environment's wonderful. 276 00:13:36,643 --> 00:13:39,728 Now we're just looking at plant size and pruning, you know, trying to 277 00:13:39,728 --> 00:13:41,168 get consistency in the structure. 278 00:13:41,173 --> 00:13:44,258 Otherwise everything else might be really, really nice. 279 00:13:45,518 --> 00:13:45,638 Jason: Yeah. 280 00:13:45,638 --> 00:13:47,978 And you know, one of the things that is so wonderful about looking 281 00:13:47,978 --> 00:13:52,744 at a confidence interval is when we talk about population statistics, we 282 00:13:52,954 --> 00:13:56,404 know that we're sampling a certain amount of plants in that room, right. 283 00:13:56,404 --> 00:14:00,154 If we've got a good, good number of sensors in there, then we should 284 00:14:00,154 --> 00:14:04,826 expect that if we go monitor any plant, it's gonna be landing within within 285 00:14:04,826 --> 00:14:06,416 that confidence interval that we see. 286 00:14:06,506 --> 00:14:06,866 So. 287 00:14:08,021 --> 00:14:08,471 Exactly. 288 00:14:08,471 --> 00:14:12,097 Like Seth said, the, the more we can shrink that the more uniform our 289 00:14:12,097 --> 00:14:15,517 crop is, is performing in that cycle. 290 00:14:18,262 --> 00:14:20,090 And then next up just to yield breakdown. 291 00:14:20,090 --> 00:14:24,025 So this is kind of cool because we can look at different options yield by 292 00:14:24,025 --> 00:14:26,695 plant yield by square foot, total yield. 293 00:14:27,055 --> 00:14:30,725 And then we get to take a look and see how much moisture loss we had in this cycle. 294 00:14:30,728 --> 00:14:31,488 How much dry weight. 295 00:14:32,243 --> 00:14:34,853 Came out of that product and how much waste comes out there as well. 296 00:14:34,853 --> 00:14:41,693 So obviously efficiency is a great way to keep track of how much biomass 297 00:14:41,693 --> 00:14:43,433 that you create as fast as possible. 298 00:14:43,438 --> 00:14:47,483 So if we are able to cut our cycle down a little bit, we're gonna see 299 00:14:47,488 --> 00:14:50,053 greater yield per square foot per day. 300 00:14:50,363 --> 00:14:55,643 So obviously the constraints of any manufacturing here is the facility size 301 00:14:55,703 --> 00:14:57,533 and the time that we're growing it. 302 00:14:57,533 --> 00:15:03,085 So if we can get more yield, we can grow things faster or increase our density of, 303 00:15:03,145 --> 00:15:05,265 of, of planting so that we get more weight 304 00:15:05,265 --> 00:15:05,685 Seth: off there. 305 00:15:08,460 --> 00:15:09,210 Absolutely. 306 00:15:09,210 --> 00:15:12,180 And if you could, Jason, could you go over, I guess on this version of the 307 00:15:12,180 --> 00:15:16,570 demo, we don't have the yield section, but I would like to do a quick preview of 308 00:15:16,593 --> 00:15:22,083 the non metric integrated yield section in production, because that is one thing 309 00:15:22,083 --> 00:15:24,903 that it's kind of tough, you know, you're usually you get your wet weight when you 310 00:15:24,908 --> 00:15:27,963 harvest, that's easy to put in and then two weeks later you get your dry weights 311 00:15:27,993 --> 00:15:31,053 or more, depending on how you buck down and how you weigh your finished product. 312 00:15:31,053 --> 00:15:33,063 If you're going on the stem off the stem. 313 00:15:34,028 --> 00:15:36,668 So a lot of times it's really easy to pass that. 314 00:15:36,848 --> 00:15:40,550 And I just wanna note that for all of our customers that aren't using metric, 315 00:15:40,550 --> 00:15:44,270 it's absolutely critical to record at least your wet weight and ideally your 316 00:15:44,270 --> 00:15:49,638 dry weight, even to populate some of the production group analytics, you know, and 317 00:15:49,643 --> 00:15:51,468 that yield entry is actually really easy. 318 00:15:51,978 --> 00:15:54,408 We're just going in for a basic wet and dry weight. 319 00:15:54,408 --> 00:15:56,658 And typically what we wanna look at wet weight, that's 320 00:15:56,658 --> 00:15:57,738 gonna be your harvest weight. 321 00:15:58,708 --> 00:16:01,318 And it's pretty self-explanatory we got dry weight, flower, weight, 322 00:16:01,318 --> 00:16:03,208 and trim rate, put trim weight. 323 00:16:03,208 --> 00:16:08,548 One thing to remember is aro is taking wet versus dry for your wet to dry ratio. 324 00:16:08,553 --> 00:16:12,808 So if you want your dry weight in analytics to represent, let's say 325 00:16:12,808 --> 00:16:17,008 bucked bud, or finished bud, that's where you want that value to go. 326 00:16:19,498 --> 00:16:24,598 And then otherwise as with everything, more data, more power in the future. 327 00:16:24,598 --> 00:16:26,068 So log as much as you can over. 328 00:16:27,328 --> 00:16:29,968 You know, you want as complete of a picture of what happened during 329 00:16:29,968 --> 00:16:32,518 that grow cycle as possible. 330 00:16:32,518 --> 00:16:34,978 And if we don't have the yield, we don't really have 331 00:16:34,978 --> 00:16:36,118 the results to compare side to. 332 00:16:37,588 --> 00:16:38,218 Jason: exactly. 333 00:16:38,218 --> 00:16:42,028 And so let's just talk a little bit about some of the, the yield numbers and, and 334 00:16:42,028 --> 00:16:43,858 why these choke points are so important. 335 00:16:44,128 --> 00:16:48,058 When we look at wet weight, obviously we're analyzing the, you know, 336 00:16:48,058 --> 00:16:50,188 the, the cultivation performance. 337 00:16:50,193 --> 00:16:51,988 How, how well did those plants grow? 338 00:16:52,078 --> 00:16:53,648 Are we at a little higher wet weight? 339 00:16:53,660 --> 00:16:56,870 And then obviously the next step is the dry weight where we able 340 00:16:56,870 --> 00:17:01,410 to retain more product simply through better drying practices. 341 00:17:01,410 --> 00:17:02,197 Did we lose. 342 00:17:03,372 --> 00:17:06,912 Did we lose more weight because maybe our plants weren't quite as dense as 343 00:17:06,912 --> 00:17:08,712 far as the, the bud structures go. 344 00:17:08,772 --> 00:17:13,522 What you know, what point did we have increase in performance or, 345 00:17:13,522 --> 00:17:15,472 or a decrease in, in yield amount? 346 00:17:15,472 --> 00:17:21,112 And obviously the sales goal is to have as much a, a class. 347 00:17:21,502 --> 00:17:22,882 Flower as we can get out of there. 348 00:17:23,092 --> 00:17:26,422 If we're doing a little bit different crop steering, we end up with a higher 349 00:17:26,422 --> 00:17:27,922 trim weight and a lower flower weight. 350 00:17:27,952 --> 00:17:31,522 Maybe that's not quite the, the route that we wanted to go with those crop steering. 351 00:17:31,522 --> 00:17:36,022 So always think about every stage of the cycle and you know, how the different 352 00:17:36,022 --> 00:17:37,972 parameters in crop steering affect 353 00:17:37,977 --> 00:17:38,602 Seth: those stages. 354 00:17:39,082 --> 00:17:39,772 Oh, absolutely. 355 00:17:39,772 --> 00:17:43,732 I mean, I could show you some beautiful graphs all day and unfortunately, the 356 00:17:43,732 --> 00:17:47,326 plants that grew from those were not what they, you know, the grower intended. 357 00:17:47,851 --> 00:17:50,941 And usually that was just because of wrong timing and switching up of 358 00:17:50,941 --> 00:17:52,875 steering techniques vegging too long. 359 00:17:52,875 --> 00:17:54,465 I mean, there's, there's a whole host of things, but that 360 00:17:54,465 --> 00:17:55,605 graph can still look beautiful. 361 00:17:55,605 --> 00:17:59,505 And if we don't evaluate the finished product, we're not really 362 00:17:59,510 --> 00:18:00,615 looking at the whole picture. 363 00:18:00,615 --> 00:18:02,325 And that's really what matters in the end. 364 00:18:03,015 --> 00:18:04,875 You know, I can sit here and tell you what I think your 365 00:18:04,875 --> 00:18:06,135 graph should look like all day. 366 00:18:06,195 --> 00:18:09,246 But if that did not produce the kind of product that you were 367 00:18:09,246 --> 00:18:10,596 looking for, then it's kind of. 368 00:18:11,796 --> 00:18:14,436 All for not, you know, there was no reason to really approach it if we 369 00:18:14,436 --> 00:18:19,236 lost quality and, you know, potentially lost market share, for instance, 370 00:18:20,436 --> 00:18:24,396 Jason: Yeah, so kind of zooming out and obviously we've got all these 371 00:18:24,396 --> 00:18:28,965 different cycles that we're cataloging into the system and, and it's nice 372 00:18:28,965 --> 00:18:30,615 to, to dig in and see, all right. 373 00:18:30,645 --> 00:18:32,145 Here's how this one ran specifically. 374 00:18:32,150 --> 00:18:33,645 Here's how, what this one ran specifically. 375 00:18:33,712 --> 00:18:37,814 Let's talk a little bit about how we know which cycles to, to dig into. 376 00:18:38,144 --> 00:18:40,664 Obviously, if it's, it's not just anecdotal, if you don't know 377 00:18:40,664 --> 00:18:42,004 exactly which one you're looking at. 378 00:18:42,169 --> 00:18:46,069 We could jump into our run analytics, or if we're trying to take a look at 379 00:18:46,069 --> 00:18:49,484 cultivar specific type of information, we can go into our cultivar 380 00:18:49,484 --> 00:18:51,314 profiles, our cultivars tab here. 381 00:18:51,314 --> 00:18:55,454 So right now I'm looking at the run analytics and this one, we have some 382 00:18:55,454 --> 00:19:00,131 options up to, to the top where we can sort each run by how it yielded per plant 383 00:19:00,131 --> 00:19:03,431 per square foot, how long it ran and. 384 00:19:04,276 --> 00:19:06,526 Then we can also categorize it by recipes. 385 00:19:06,616 --> 00:19:09,766 So maybe if I had a little bit different crop steering recipe that I've tried 386 00:19:09,766 --> 00:19:13,696 a few times, we can compare those and say, all right, well, when we're 387 00:19:13,696 --> 00:19:17,707 running in our, our heart steering you know, let's say it's like, we, 388 00:19:18,187 --> 00:19:22,297 we can name these, you know, the Ferrari recipe versus the Jeep recipe. 389 00:19:22,335 --> 00:19:23,129 Well we know, Hey. 390 00:19:23,319 --> 00:19:25,119 We're running there for your recipe. 391 00:19:25,149 --> 00:19:27,826 Maybe it takes a little bit more work for our part, but every time 392 00:19:27,826 --> 00:19:30,016 we run that we get 20% more yield. 393 00:19:30,086 --> 00:19:34,016 So yeah, this list here is all the harvest groups and we can take a look and we 394 00:19:34,016 --> 00:19:35,569 know what what we were running in there. 395 00:19:35,569 --> 00:19:38,809 How many plants, what recipes we used, how many alerts? 396 00:19:38,941 --> 00:19:42,151 Anytime we did IPM event, the duration. 397 00:19:42,846 --> 00:19:45,486 Really just trying to give us an overview of what happened in 398 00:19:45,491 --> 00:19:47,016 order to, to hit that wet weight. 399 00:19:47,021 --> 00:19:51,186 So if I've got a whole list of this and I want to check in there and I can 400 00:19:51,186 --> 00:19:55,222 sort by our wet weight, let's get the, the run with the highest wet weight. 401 00:19:55,797 --> 00:20:00,057 And then take a look and see what type of parameters that we 402 00:20:00,062 --> 00:20:01,287 ran in order to get that weight. 403 00:20:01,857 --> 00:20:03,807 Was it the great, great genetics? 404 00:20:03,807 --> 00:20:06,087 Well then let's jump into C our profiles. 405 00:20:06,387 --> 00:20:10,707 Was it something that we did specific on purpose or is a mistake in 406 00:20:10,707 --> 00:20:13,887 this harvest group to, to get that different yield and let's take a look 407 00:20:13,887 --> 00:20:15,537 and see, you know, what happened? 408 00:20:15,537 --> 00:20:18,567 Why, why did we get more, more out of that? 409 00:20:18,687 --> 00:20:21,717 So that's where I like to dig in and say, all right, let's pick out specifically. 410 00:20:21,722 --> 00:20:23,547 Good runs specifically bad runs. 411 00:20:24,097 --> 00:20:29,977 And understand what effects made that such a successful or unsuccessful run. 412 00:20:30,667 --> 00:20:30,937 Seth: Yeah. 413 00:20:30,937 --> 00:20:33,577 And that's where, you know, we always stress on crop registration and 414 00:20:33,582 --> 00:20:36,602 organized note taking that's, that's where this comes in, when you're looking 415 00:20:36,602 --> 00:20:39,932 back and really trying to evaluate what happened on a great crop, you 416 00:20:39,937 --> 00:20:41,282 want to capture every part of it. 417 00:20:41,287 --> 00:20:43,082 And that part of that includes your plant height. 418 00:20:43,592 --> 00:20:47,132 Any other manual readings you wanna take, including notes, basing stem, diameter, 419 00:20:47,792 --> 00:20:51,842 and then, you know, going down the line and just taking as many notes as possible. 420 00:20:51,842 --> 00:20:52,802 Take those pictures. 421 00:20:53,317 --> 00:20:54,847 Record as much as you can. 422 00:20:54,847 --> 00:20:58,777 That's how we can look back and really quantify this and evaluate it without 423 00:20:58,777 --> 00:21:00,217 that little bit of information. 424 00:21:01,087 --> 00:21:04,087 It may seem small on a daily basis, cuz you're just, it's 10 425 00:21:04,092 --> 00:21:06,727 seconds or 30 seconds of your time to enter some of these readings. 426 00:21:06,727 --> 00:21:11,737 But cumulatively, after you have failed to enter, let's say 60 of them or 63 427 00:21:12,337 --> 00:21:16,117 throughout a growth phase, suddenly you're blind to a certain point that you had and 428 00:21:16,117 --> 00:21:18,157 all you have then is kind of going well. 429 00:21:18,157 --> 00:21:19,417 We, we did take some points. 430 00:21:19,417 --> 00:21:21,817 We can make some basic assumptions on that, but. 431 00:21:22,507 --> 00:21:26,257 Unless we really capture that we don't, we can't responsibly make certain 432 00:21:26,257 --> 00:21:28,147 assumptions based on a lack of data. 433 00:21:29,812 --> 00:21:30,772 . Jason: Yeah, that's a great point. 434 00:21:31,042 --> 00:21:35,002 Some of our best clients are going in there every single day, taking a picture. 435 00:21:35,182 --> 00:21:38,692 And it's really fun to go back and look at the growth cycle and they could say, Hey, 436 00:21:39,082 --> 00:21:42,352 this is, you know, a specific spot maybe where we started seeing Herms going on. 437 00:21:42,412 --> 00:21:46,238 Let's compare to the data and understand what might have induced that maybe we see 438 00:21:46,238 --> 00:21:48,068 some Fox ceiling going on towards the end. 439 00:21:48,338 --> 00:21:51,977 Let's check out our EC, our irrigation patterns that that might have been making. 440 00:21:52,667 --> 00:21:54,614 That growth behavior, the way it was. 441 00:21:54,794 --> 00:21:58,994 So we always encourage people, you know, have, have a team that when 442 00:21:58,994 --> 00:22:02,864 they're in the room every day, take some, some notes on, you know, could 443 00:22:02,864 --> 00:22:05,654 even just be a check mark where you're saying, Hey, everything looks good. 444 00:22:05,659 --> 00:22:06,554 Let's take a picture. 445 00:22:07,004 --> 00:22:12,044 And the wonderful thing about having that database of pictures is when you go to 446 00:22:12,044 --> 00:22:13,946 run that cold fire again, and you kind of. 447 00:22:13,970 --> 00:22:18,849 Visually what your expectations are, and if you aren't running a cultivar 448 00:22:18,849 --> 00:22:23,469 very often, you can go back and make sure and say, Hey, our, our purple 449 00:22:23,469 --> 00:22:25,226 punch is is doing that weird thing. 450 00:22:25,226 --> 00:22:26,726 Did it do it last time as well? 451 00:22:26,726 --> 00:22:31,474 Is that just how this genetic expresses itself or is something something goofy 452 00:22:31,479 --> 00:22:35,240 going on that we need to correct in order to avoid that that visual appeal of the. 453 00:22:36,305 --> 00:22:37,085 Seth: Oh, absolutely. 454 00:22:37,085 --> 00:22:39,695 And even for continuity in your organization, you know, I've 455 00:22:39,695 --> 00:22:42,215 definitely grown some strains that were in the facility when I came 456 00:22:42,215 --> 00:22:46,505 in that behave strangely and it's first instinct is kind of freak out. 457 00:22:46,655 --> 00:22:48,005 You're like, what is this plant doing? 458 00:22:48,005 --> 00:22:52,025 Well, if I have a repository of information about that particular 459 00:22:52,030 --> 00:22:55,940 culture, where I can look back and go, oh, This has done that regularly. 460 00:22:55,940 --> 00:22:59,750 I don't need to freak out about that or this has happened, but also, you 461 00:22:59,750 --> 00:23:02,810 know, this, like we have one strain that Foxtails, let's say, okay, 462 00:23:02,840 --> 00:23:06,170 well every run that it's foxtail, man, we can't keep the temperature 463 00:23:06,170 --> 00:23:07,910 under like 85 at the end of flower. 464 00:23:07,915 --> 00:23:09,320 Like, okay, well there we go. 465 00:23:09,380 --> 00:23:11,120 Or, Hey, everything was great. 466 00:23:11,120 --> 00:23:14,990 And this thing still Fox tailed like, oh, well maybe it's 467 00:23:14,990 --> 00:23:17,330 got kind of some undesirable traits then that we don't like. 468 00:23:17,330 --> 00:23:21,590 And instead of wasting time trying to manipulate different variables, we might 469 00:23:21,590 --> 00:23:23,210 just decide that's not a strain for us to. 470 00:23:24,270 --> 00:23:27,896 Jason: Yeah anecdotally I was thinking about so like Kim, Kim dog, we used 471 00:23:27,896 --> 00:23:31,948 to run and it it was varied so that sometimes every once in a while, 472 00:23:31,948 --> 00:23:35,032 you'd have part of the half the leaf that that would be lighter in 473 00:23:35,032 --> 00:23:36,652 colors and a little bit of striation. 474 00:23:37,387 --> 00:23:40,687 and this is a great example where, you know, if it's first time you saw that in 475 00:23:40,687 --> 00:23:43,898 the plant, we could go back and look at the history of it and we'd know that, you 476 00:23:43,898 --> 00:23:46,838 know, it wasn't something like tobacco, mosaic virus, that's hitting the plant. 477 00:23:47,108 --> 00:23:49,478 We just know that it's the properties of that genetic cuz 478 00:23:49,478 --> 00:23:51,158 we've seen it historically as well. 479 00:23:51,488 --> 00:23:55,658 So all this information going into to detail that the different cult 480 00:23:55,658 --> 00:23:59,138 bars brings a ton of value to what you're doing on a daily basis. 481 00:24:00,353 --> 00:24:00,773 Seth: Oh, yeah. 482 00:24:00,833 --> 00:24:03,023 You know, I mean, there's certain stuff, especially in the cannabis 483 00:24:03,023 --> 00:24:06,113 world, you know, I mean, we, when I get it cut, oftentimes I don't know 484 00:24:06,113 --> 00:24:09,533 when that seed was popped, unless the person I got it from popped the seed. 485 00:24:09,533 --> 00:24:14,663 Some of these might have been, you know, in propagation for many, many 486 00:24:14,663 --> 00:24:16,583 generations, you know, 20 plus years. 487 00:24:16,583 --> 00:24:20,423 So sometimes we do see things like just kinda like that slight variation, which 488 00:24:20,423 --> 00:24:23,843 is generally a somatic mutation in that chem dog that it's susceptible to. 489 00:24:24,923 --> 00:24:28,433 If you know that that's happening, just like Jason said, you can kind of 490 00:24:28,433 --> 00:24:31,943 prepare for it and also go, okay, well, it, it does that and we've accepted it. 491 00:24:32,003 --> 00:24:32,633 We're moving on. 492 00:24:32,723 --> 00:24:35,333 Does that bother us as growers or does that bother the consumer? 493 00:24:36,263 --> 00:24:39,714 And you can really start to narrow in, on some of those, those things, 494 00:24:39,714 --> 00:24:44,484 especially just because again, cyclically so much happens that I don't, I don't 495 00:24:44,484 --> 00:24:47,424 wanna say anyone has a bad memory, but it's really, really quick in a 496 00:24:47,424 --> 00:24:50,814 commercial situation where almost anyone in this conversation here, you know, 497 00:24:50,814 --> 00:24:52,404 growing 50, 60, a hundred thousand. 498 00:24:54,054 --> 00:24:54,684 10 years in. 499 00:24:54,684 --> 00:24:55,524 That was a long time ago. 500 00:24:56,194 --> 00:25:00,954 that you hit that number, you know, so it's, it's all about that registration. 501 00:25:02,184 --> 00:25:03,294 Kaisha: Knowledge really is power. 502 00:25:03,294 --> 00:25:03,774 Isn't it? 503 00:25:03,779 --> 00:25:06,290 I, I, this is so, comprehensive. 504 00:25:06,320 --> 00:25:09,350 I actually was wondering, we get a lot of questions. 505 00:25:09,416 --> 00:25:12,433 Oh, Michael, just ask question, Michael, make it to your question next, but we 506 00:25:12,433 --> 00:25:17,563 get a lot of questions about ideal, like EC ranges, for example, can this 507 00:25:17,743 --> 00:25:21,403 particular, the analytics tool kind of help people identify trends with 508 00:25:21,403 --> 00:25:22,783 what's going on with their particular. 509 00:25:24,493 --> 00:25:25,403 Seth: Yeah, absolutely. 510 00:25:25,403 --> 00:25:27,875 Especially if you're, you know, keeping track of your plant health, 511 00:25:27,875 --> 00:25:31,445 taking pictures, taking notes on anything strange that might happen. 512 00:25:31,445 --> 00:25:34,925 And then also, you know, when you do take those notes, make sure you're complete, 513 00:25:34,930 --> 00:25:38,315 you know, if you think it's a nutrient deficiency, do your spot, check that day, 514 00:25:38,315 --> 00:25:42,575 check your feed, see, check your runoff, get as complete of a picture as you can. 515 00:25:42,580 --> 00:25:45,755 Because as far as EC ranges go, that's something we're dialing 516 00:25:45,755 --> 00:25:47,075 for every specific strain. 517 00:25:47,645 --> 00:25:49,385 The range on 'em is quite wide and. 518 00:25:50,285 --> 00:25:52,295 At the end of the day, I'll say it, they call it weed for a reason. 519 00:25:52,295 --> 00:25:53,315 It's very adaptable. 520 00:25:53,945 --> 00:25:57,185 I can take the same strain and actually grow it at two different EC levels. 521 00:25:57,185 --> 00:26:00,455 And as long as I apply that EC in the right manner, I'm gonna 522 00:26:00,455 --> 00:26:01,715 get a really similar result. 523 00:26:01,715 --> 00:26:03,485 It's about adapting the plant to that. 524 00:26:04,295 --> 00:26:07,069 And that's where we can start to kind of catalog this stuff. 525 00:26:07,069 --> 00:26:09,259 We can say, okay, here was the graph we had. 526 00:26:09,289 --> 00:26:10,129 Here's some pictures. 527 00:26:10,129 --> 00:26:10,939 Here's the yield. 528 00:26:11,269 --> 00:26:11,989 Here's our quality. 529 00:26:11,989 --> 00:26:13,309 What, what can I deduce from that? 530 00:26:13,309 --> 00:26:15,289 The higher EC run gave me better quality. 531 00:26:15,319 --> 00:26:15,679 Cool. 532 00:26:15,739 --> 00:26:19,249 I'm gonna go with higher EC the lower EC run gave me better yield. 533 00:26:19,654 --> 00:26:20,584 And the same quality. 534 00:26:20,614 --> 00:26:20,884 Cool. 535 00:26:20,944 --> 00:26:22,334 We're gonna go at the lower EC run. 536 00:26:22,363 --> 00:26:26,503 There are no hard, fast rules on EC, other than, you know, there, there are 537 00:26:26,593 --> 00:26:29,983 some definite upward limits where you start to get to a point where a plant 538 00:26:29,983 --> 00:26:31,813 can't actually live in that salty water. 539 00:26:32,563 --> 00:26:36,313 But as far as basic ranges go, I mean, it's, it's very, very wide and 540 00:26:36,318 --> 00:26:40,813 it has a lot more to do again about application of that EC over time 541 00:26:40,813 --> 00:26:43,511 and how well you're adapting your plant to live in that environment. 542 00:26:43,540 --> 00:26:45,820 Kaisha: One of our attendees posted question here, and Michael, 543 00:26:45,820 --> 00:26:46,840 you're welcome to unmute yourself. 544 00:26:46,840 --> 00:26:50,680 You wanna add to it, but he's asking if we can cover dry weight analytics 545 00:26:50,680 --> 00:26:54,310 grams per square foot wet versus dry weight retention, et cetera. 546 00:26:56,740 --> 00:26:57,220 Jason: Sure. 547 00:26:57,356 --> 00:26:57,626 To it. 548 00:26:57,626 --> 00:26:59,726 Should I bulb the interface here and we can show again? 549 00:26:59,731 --> 00:26:59,826 Yeah. 550 00:26:59,826 --> 00:27:00,026 All 551 00:27:01,856 --> 00:27:05,096 Seth: just look at like facility performance and start going from there. 552 00:27:08,156 --> 00:27:11,126 Touch on a little bit about how we gather these analytics and what 553 00:27:11,126 --> 00:27:12,326 they mean when we're looking at it. 554 00:27:15,641 --> 00:27:15,911 Jason: Yeah. 555 00:27:15,911 --> 00:27:20,305 So, let's just get started obviously some of this yield information here, if we're 556 00:27:20,305 --> 00:27:24,730 in the facility performance page, we can take a look at yields total per cycle per 557 00:27:24,730 --> 00:27:26,470 total, per square foot, total per plant. 558 00:27:26,470 --> 00:27:30,445 We can to do some by wet weight, dry weight waste amounts. 559 00:27:30,715 --> 00:27:33,767 So, you know, as far as driveway specifically Obviously some 560 00:27:33,767 --> 00:27:36,587 people do a little bit of different types of benchmarks. 561 00:27:36,587 --> 00:27:39,467 When we talk about dry weight, you know, is it dry weight on stem? 562 00:27:39,467 --> 00:27:40,997 Is it dry weight after we've bucked? 563 00:27:40,997 --> 00:27:43,547 It does our dry weight include trim. 564 00:27:43,947 --> 00:27:47,177 And that's something that we haven't necessarily made a clear 565 00:27:47,177 --> 00:27:48,437 definition in the software. 566 00:27:48,437 --> 00:27:52,887 It's and there's a good reason why, because we want you guys to keep doing it 567 00:27:52,887 --> 00:27:54,407 the way that you have been tracking it. 568 00:27:54,407 --> 00:27:57,647 So your, your yield information is consistently comparable 569 00:27:57,647 --> 00:27:58,687 throughout, throughout the. 570 00:27:59,122 --> 00:28:03,482 And so obviously when you do go in here, keep in mind with your staff, 571 00:28:03,482 --> 00:28:05,492 how, how are they, they tracking this? 572 00:28:05,492 --> 00:28:10,935 What, what, what is the reason that you're looking at that specific group of 573 00:28:10,935 --> 00:28:11,985 Seth: weight, if you will. 574 00:28:15,105 --> 00:28:15,375 Yeah. 575 00:28:15,375 --> 00:28:17,835 And I mean, one thing to touch on really grants for square foot 576 00:28:17,835 --> 00:28:20,355 or Roy is calculating that based on your zone square footage. 577 00:28:20,355 --> 00:28:21,135 So that's looking at. 578 00:28:22,325 --> 00:28:24,785 Basically how much yield you're reporting off of that room. 579 00:28:24,875 --> 00:28:28,295 If we have one harvest group, one room, we're gonna take that total yield, divide 580 00:28:28,295 --> 00:28:31,535 that by the number of actual canopy square feet, we have pull that number. 581 00:28:32,225 --> 00:28:35,285 And then as far as wet versus dry retention, that's straight up that 582 00:28:35,285 --> 00:28:37,235 ratio we're looking at at 102. 583 00:28:37,760 --> 00:28:39,900 10 20, 30, whatever it ends up being. 584 00:28:39,900 --> 00:28:42,054 That's, that's where that is being pulled from. 585 00:28:42,084 --> 00:28:45,834 As far as drying goes, though, we do always try to point towards, and 586 00:28:45,834 --> 00:28:49,914 it's a manual entry right now, water activity, as a marker of when your 587 00:28:49,914 --> 00:28:51,594 plant is actually ready to come down. 588 00:28:51,599 --> 00:28:55,494 And, you know, I mean also when we're talking about dry weight, when important 589 00:28:55,499 --> 00:28:59,304 thing to remember is although we're using a water activity meter to really 590 00:28:59,304 --> 00:29:00,804 determine when that plant is done. 591 00:29:02,559 --> 00:29:04,329 That doesn't mean it's always done curing. 592 00:29:04,539 --> 00:29:08,829 And part of that curing process is homogenization of moisture inside the bud. 593 00:29:08,829 --> 00:29:11,169 So we're gonna have parts of the bud that are dryer parts of the bud 594 00:29:11,169 --> 00:29:12,999 that are less dry that's partially. 595 00:29:12,999 --> 00:29:13,209 Why. 596 00:29:13,239 --> 00:29:13,449 Okay. 597 00:29:13,449 --> 00:29:17,649 Maybe if we hit that 0.6 water activity, someone goes and tries to smoke that 598 00:29:17,649 --> 00:29:19,149 bud part of it burns in the inside. 599 00:29:19,154 --> 00:29:21,309 Doesn't very well while it's water on the inside. 600 00:29:21,309 --> 00:29:21,639 So. 601 00:29:22,344 --> 00:29:26,094 That's a very dynamic measurement that you want to take over time. 602 00:29:26,094 --> 00:29:29,064 And although there might not be a lot of variation in it, we might 603 00:29:29,064 --> 00:29:32,904 wanna expect it to stay at 0.6 over a long period of time, not return 604 00:29:32,909 --> 00:29:34,284 to it and retest it and go, okay. 605 00:29:34,284 --> 00:29:38,484 Now I've only got 0.5 after we've even out the content in this material. 606 00:29:38,484 --> 00:29:38,934 So. 607 00:29:39,969 --> 00:29:41,439 dry weight is very important. 608 00:29:41,527 --> 00:29:44,437 Looking at your ratio as Jason saying earlier of, you know, 609 00:29:44,557 --> 00:29:46,327 flower to trim, that's huge. 610 00:29:47,377 --> 00:29:48,097 We're looking at that. 611 00:29:48,097 --> 00:29:51,067 That's gonna make a lot of steering decisions for us and potentially 612 00:29:51,067 --> 00:29:52,147 genetic decisions as well. 613 00:29:53,467 --> 00:29:56,917 Jason: Oh, one of the things that I used to find is I would build out my 614 00:29:56,917 --> 00:30:01,326 projections and through each stage when I was capturing you know, a choke 615 00:30:01,326 --> 00:30:03,531 point of information, Wet weight. 616 00:30:03,531 --> 00:30:06,964 For example, I go back and kind of reprocessed my projections. 617 00:30:07,264 --> 00:30:12,786 So we, you know, we know we're hitting saved 18% retention from our wet weight. 618 00:30:13,116 --> 00:30:15,876 And we've got some new numbers for our wet weight coming in. 619 00:30:15,881 --> 00:30:19,746 Well, if we've got a higher wet weight, then we had projected for now, we can 620 00:30:19,746 --> 00:30:24,246 rerun those numbers and, and kind of get closer and closer to the exact 621 00:30:24,456 --> 00:30:26,016 amount that we're gonna be pulling down. 622 00:30:27,276 --> 00:30:27,456 Seth: Yeah. 623 00:30:27,456 --> 00:30:29,796 And that's, that's the goal of all this in the end, right. 624 00:30:29,796 --> 00:30:31,026 Is to be able to predict how. 625 00:30:31,611 --> 00:30:33,891 How much product we're gonna have for sale in a few months. 626 00:30:33,891 --> 00:30:37,461 Like if we can't do that, it makes it really hard to run a business. 627 00:30:37,461 --> 00:30:41,384 You know, any kind of manufacturing process is most profitable when you can 628 00:30:41,504 --> 00:30:43,634 monitor your inputs versus your outputs. 629 00:30:43,694 --> 00:30:47,444 And if we're not looking at what our outputs are, especially in terms of 630 00:30:47,444 --> 00:30:51,584 what we have as saleable product, then it's really hard to get an eye. 631 00:30:52,829 --> 00:30:54,149 How successful are we being? 632 00:30:54,269 --> 00:30:55,859 Cause yeah, at the end of the day, it all comes down to that. 633 00:30:56,249 --> 00:30:59,639 Not just the yield and weight, obviously the quality, but the final product. 634 00:30:59,639 --> 00:31:03,629 If that's not what we need to sell it, wasn't worth it to grow it. 635 00:31:03,634 --> 00:31:07,139 And that's where we've really gotta kind of narrow that in and make sure 636 00:31:07,139 --> 00:31:11,699 we're, we're creating a product that your company is able to sell and 637 00:31:11,699 --> 00:31:13,049 is the product that you wanna sell 638 00:31:14,279 --> 00:31:14,579 Jason: cat. 639 00:31:14,579 --> 00:31:20,028 And, you know, as with, with any any cyclical growing cycles, we are. 640 00:31:20,718 --> 00:31:25,308 Trying to get as much data and so that we can really dial in 641 00:31:25,398 --> 00:31:26,748 what those projections look like. 642 00:31:27,108 --> 00:31:31,248 If you've only got maybe three runs on a specific strain, we can't expect our 643 00:31:31,277 --> 00:31:35,500 projections to be nearly as accurate as if we've got 30 or 40 runs on a strain. 644 00:31:35,860 --> 00:31:39,281 And, you know, kind of looks like that, that confidence in a role, our confidence 645 00:31:39,281 --> 00:31:42,701 band, I was talking about where the, the more data that we've captured, 646 00:31:42,971 --> 00:31:46,724 the, the tighter that we, we know we're gonna hit with with that product coming. 647 00:31:48,194 --> 00:31:48,374 Kaisha: Yeah. 648 00:31:48,374 --> 00:31:50,834 At the end of the day, it's ensuring the longevity of your business. 649 00:31:50,834 --> 00:31:51,194 Right? 650 00:31:51,194 --> 00:31:54,998 So kind of updating the skillset all these talented growers out there just 651 00:31:54,998 --> 00:31:58,298 like work with the data to help you achieve the goals that you wanna achieve. 652 00:31:58,478 --> 00:32:00,228 Michael, thank you so much for your question. 653 00:32:00,230 --> 00:32:02,600 The questions are not really coming in live that's okay. 654 00:32:02,780 --> 00:32:06,315 I have one more question here and you know, we'll see if some more come in. 655 00:32:06,339 --> 00:32:07,929 I was just wondering if there's a. 656 00:32:08,599 --> 00:32:11,479 Feature within the harvest group analytics that you guys wish 657 00:32:11,479 --> 00:32:13,099 more clients took advantage of. 658 00:32:13,165 --> 00:32:15,955 Maybe they're not aware of it or forget about it, but yeah. 659 00:32:15,955 --> 00:32:19,533 What would you like, what would you like to see more clients really utilize and, 660 00:32:19,533 --> 00:32:22,034 and embrace in their business practices? 661 00:32:23,084 --> 00:32:23,634 Jason: Pictures? 662 00:32:23,634 --> 00:32:26,300 I used to take a ton of pictures as a cultivator was something 663 00:32:26,300 --> 00:32:27,650 I really, really enjoyed. 664 00:32:27,950 --> 00:32:29,791 And it's something that kind of brought me to the point where. 665 00:32:30,531 --> 00:32:33,801 Train my brain into picking out any mistakes in the garden. 666 00:32:33,801 --> 00:32:37,284 If my fer was labeled wrong, we took some cuts off the wrong mom, 667 00:32:37,554 --> 00:32:38,844 any of those type of mistakes. 668 00:32:38,853 --> 00:32:43,036 The more pictures you take, the better visual recognition you have of what to, 669 00:32:43,366 --> 00:32:45,436 what to expect throughout that grow cycle. 670 00:32:46,736 --> 00:32:48,046 Seth: Yeah, pictures. 671 00:32:48,166 --> 00:32:48,526 Great. 672 00:32:48,556 --> 00:32:50,236 I, I was personally gonna say manual reading. 673 00:32:50,866 --> 00:32:55,354 You know, I've definitely noticed a habit among some growers, especially 674 00:32:55,354 --> 00:32:59,016 once they get you know, electronic data logging involved to kind of veer 675 00:32:59,016 --> 00:33:02,076 away from taking spot measurements and taking the daily notes that they need 676 00:33:02,081 --> 00:33:03,927 to aro makes that incredibly easy. 677 00:33:04,257 --> 00:33:08,997 To just do that on your phone or your tablet or the computer either way, 678 00:33:08,997 --> 00:33:12,597 but the plus button plus add a note or add a reading is very easy to use. 679 00:33:12,597 --> 00:33:16,377 It's not time consuming and it's skipping that step of like, I, I 680 00:33:16,382 --> 00:33:18,927 used to take notes, but they didn't always make it into the computer. 681 00:33:19,617 --> 00:33:20,847 Now we're dumping it right. 682 00:33:20,847 --> 00:33:22,987 All into one spot where we can access it later. 683 00:33:22,998 --> 00:33:25,938 That's, that's a huge thing I found, right, right up there with not taking 684 00:33:25,938 --> 00:33:30,516 pictures and not keeping just general track of cultivation processes. 685 00:33:30,517 --> 00:33:33,577 I, but personally I would love to see more of my customers use those manual 686 00:33:33,577 --> 00:33:36,937 readings, cuz that gives us, that's all the things we can't see with AUR 687 00:33:37,297 --> 00:33:38,527 and they're all there for a reason. 688 00:33:38,647 --> 00:33:39,547 They're all important. 689 00:33:39,547 --> 00:33:42,757 And if you want a holistic look at what's going on, we need 690 00:33:42,757 --> 00:33:44,107 to see all those variables. 691 00:33:46,552 --> 00:33:47,002 Kaisha: Amazing. 692 00:33:47,002 --> 00:33:47,782 I'm here for it. 693 00:33:47,830 --> 00:33:50,020 You guys, we don't have any live questions coming in. 694 00:33:50,020 --> 00:33:53,020 I guess everybody's all set on crop steering and sensors. 695 00:33:53,070 --> 00:33:55,594 But yeah, this is a great overview actually. 696 00:33:55,594 --> 00:33:58,384 So, you know, aro customers definitely take full advantage 697 00:33:58,384 --> 00:33:59,674 of your harvest group analytics. 698 00:33:59,674 --> 00:34:03,134 There's so much that can be learned from that. 699 00:34:03,134 --> 00:34:05,191 I think we're gonna wrap it up a little bit early. 700 00:34:05,191 --> 00:34:08,701 Seth, Jason, anything you wanna say before we, before we go. 701 00:34:11,266 --> 00:34:12,646 Jason: I think we're good over here. 702 00:34:12,736 --> 00:34:13,006 Seth: Yeah. 703 00:34:13,006 --> 00:34:14,776 Keep, keep having fun growing out there. 704 00:34:14,836 --> 00:34:18,856 We're I know I'm always stoked to be part of an industry where all my customers are 705 00:34:18,856 --> 00:34:20,506 also stoked to be part of the industry. 706 00:34:20,746 --> 00:34:20,806 Yeah. 707 00:34:20,811 --> 00:34:23,176 It's always great interacting with our customers and we love it. 708 00:34:23,836 --> 00:34:24,946 Kaisha: I so agree with that. 709 00:34:24,946 --> 00:34:27,568 Yeah, no, we, we this is a dynamic exciting industry. 710 00:34:27,568 --> 00:34:29,968 It could be frustrating at times, but I don't know. 711 00:34:29,968 --> 00:34:31,078 I don't wanna be anywhere else. 712 00:34:31,078 --> 00:34:31,798 How about you guys? 713 00:34:33,793 --> 00:34:34,393 Seth: Not really. 714 00:34:34,933 --> 00:34:38,323 There's far less, far less exciting parts of agriculture to be in that's for sure. 715 00:34:38,323 --> 00:34:38,443 Yeah. 716 00:34:40,063 --> 00:34:40,543 Kaisha: that's it. 717 00:34:40,603 --> 00:34:41,053 Wow. 718 00:34:41,773 --> 00:34:42,433 Thank you. 719 00:34:42,438 --> 00:34:42,763 Set. 720 00:34:42,853 --> 00:34:44,963 And Jason so much for a great conversation. 721 00:34:44,963 --> 00:34:47,287 Thanks to everybody who joined us live today. 722 00:34:47,358 --> 00:34:50,679 Anybody you know, who's never seen us before we do this every Thursday. 723 00:34:50,769 --> 00:34:53,499 And the best way to get answers from the experts is to join live. 724 00:34:53,499 --> 00:34:56,467 So definitely feel free to join us live every week. 725 00:34:56,473 --> 00:34:58,843 If you have any questions about AROYA, feel free to book a demo. 726 00:34:58,848 --> 00:35:01,453 Our experts will tell you about how it can be used to improve your 727 00:35:01,453 --> 00:35:03,193 cultivation production process. 728 00:35:03,523 --> 00:35:06,073 And then as always, if there's a topic you'd like us to cover in a 729 00:35:06,073 --> 00:35:10,093 future office hours episode, posted in the chat, shoot us an email 730 00:35:10,093 --> 00:35:14,258 at support.aroya@metergroup.com or send us a DM of our Instagram. 731 00:35:14,258 --> 00:35:15,518 We definitely wanna hear from you. 732 00:35:15,818 --> 00:35:16,808 We record every session. 733 00:35:16,808 --> 00:35:18,908 We'll email everybody in attendance, a link to the video 734 00:35:18,908 --> 00:35:20,168 from today's conversation. 735 00:35:20,348 --> 00:35:23,078 It'll also be on the AROYA YouTube channel, like subscribe 736 00:35:23,083 --> 00:35:24,638 and share while you are there. 737 00:35:24,938 --> 00:35:27,968 And if these conversations are helpful, do spread the word. 738 00:35:28,448 --> 00:35:29,288 Thank you all so much. 739 00:35:29,293 --> 00:35:32,048 Seth and Jason, I look forward to seeing you next week in person. 740 00:35:32,623 --> 00:35:34,123 I'll be in Pullman, Washington. 741 00:35:34,543 --> 00:35:35,023 Seth: Awesome. 742 00:35:35,293 --> 00:35:35,533 Yeah. 743 00:35:35,533 --> 00:35:37,183 That's pretty exciting case you can't wait to see here. 744 00:35:37,423 --> 00:35:37,633 Can't 745 00:35:37,633 --> 00:35:37,843 Kaisha: wait. 746 00:35:37,843 --> 00:35:38,953 I have never met these guys in. 747 00:35:39,103 --> 00:35:39,313 Oh wait. 748 00:35:39,313 --> 00:35:39,913 No, that's not true. 749 00:35:39,913 --> 00:35:42,913 I haven't met Jason, everybody. 750 00:35:42,913 --> 00:35:43,843 Thank you so much, 751 00:35:45,373 --> 00:35:46,823 Seth: Kaisha.