1
00:00:00,100 --> 00:00:10,140
Ping Yu: (on-hold music) Hello everyone, and welcome to the Blooms and Beyond Podcast, a podcast that uncovers

2
00:00:10,240 --> 00:00:13,300
plant history, culture, and management through the lens of science.

3
00:00:13,740 --> 00:00:14,480
I'm your host, Ping.

4
00:00:14,760 --> 00:00:15,900
How's everyone doing today?

5
00:00:16,360 --> 00:00:25,860
I'm doing great because, uh, I'm going to pick up the conversation where we left off last week with, of course, two of my favorite people, Dr. James Robbins and

6
00:00:25,900 --> 00:00:27,560
Dr. Joe Maja, on the drones.

7
00:00:28,160 --> 00:00:37,140
Last week, in the first part of our conversation, we got into some basics about drones in the nursery and greenhouse, types, licensing, and many others.

8
00:00:37,620 --> 00:00:47,040
This week in part two, we're going into more drone applied research, including using RFID tags for inventory and, of course, more.

9
00:00:47,520 --> 00:00:50,480
So without further ado, let's just jump right into it.

10
00:00:50,660 --> 00:00:53,800
Here is a conversation with James and Joe in part two.

11
00:00:54,020 --> 00:00:55,020
I hope you enjoy it.

12
00:00:57,620 --> 00:01:02,980
(on-hold music) Let's circle back to one of the projects that you guys are doing right now.

13
00:01:03,160 --> 00:01:09,460
It's the RFID project, and I know for that, there's a lot of, like, labeling.

14
00:01:10,320 --> 00:01:17,540
Um, that kind of-- the labeling plays a significant role in terms of the, like, RFID.

15
00:01:17,660 --> 00:01:27,920
Can you tell us a little bit about the RFID project and what role does the labeling play in terms of, like, for a commercial

16
00:01:28,200 --> 00:01:32,060
nursery and in terms of the drone application in general?

17
00:01:34,200 --> 00:01:43,270
Jim Robbins: So let's go back to the original, again, our evolution, because Joe and I have been involved in trying to find an automated way to improve the inventory

18
00:01:43,360 --> 00:01:46,880
process in nurseries for many years, and we tried ground-based.

19
00:01:48,240 --> 00:01:54,060
When we first kind of thought about-- for many years, we've thought about the RFID tags.

20
00:01:54,300 --> 00:01:58,260
So just think about it: it has to have line of sight.

21
00:01:58,730 --> 00:01:59,100
And so

22
00:02:00,900 --> 00:02:03,100
containers are jammed together, that's just...

23
00:02:04,760 --> 00:02:09,460
Until you, you know, put them on a conveyor belt or something where you can get them all...

24
00:02:11,090 --> 00:02:13,600
The brilliance-- the advantage

25
00:02:15,600 --> 00:02:18,660
is that it doesn't require line of, line of sight.

26
00:02:20,640 --> 00:02:22,580
And if we could attach

27
00:02:24,320 --> 00:02:26,180
the plant, whether that...

28
00:02:28,160 --> 00:02:34,660
And then just fly over the top and simply count these things, we thought this was brilliant.

29
00:02:35,580 --> 00:02:40,460
And then we also envisioned a more complete system.

30
00:02:40,580 --> 00:02:46,430
So let's say you're a wholesale producer and you put-- at the time of canning, you put a tag on the container,

31
00:02:48,540 --> 00:02:50,570
the beds, the container beds.

32
00:02:51,260 --> 00:02:52,660
And again, you're keeping in...

33
00:02:54,720 --> 00:02:57,300
A simple antenna at the back of the truck.

34
00:02:57,420 --> 00:03:06,859
And so as it goes onto the truck, probably on a conveyor, it would be read, and a very accurate inventory would occur.

35
00:03:07,020 --> 00:03:14,480
Maybe as a double check for, you know, manual methods, but touchless-- so touchless inventory.

36
00:03:14,560 --> 00:03:18,400
That truck then goes to a garden center, comes off and,

37
00:03:20,480 --> 00:03:22,420
and recode that same tag.

38
00:03:23,040 --> 00:03:24,320
So they're gonna then...

39
00:03:26,280 --> 00:03:27,049
Some information.

40
00:03:27,120 --> 00:03:30,440
That'd be their inventory number or their price, et cetera.

41
00:03:31,080 --> 00:03:41,400
And at checkout, they would use, again, a simple, either handheld scanner like we currently use for barcode, or they would use an antenna in a portal.

42
00:03:41,500 --> 00:03:44,870
So as they walk out, it's again-- like some of these stores,

43
00:03:46,860 --> 00:03:50,360
Amazon or whoever has these stores where there's no workers there.

44
00:03:50,460 --> 00:03:51,750
You just walk out.

45
00:03:51,920 --> 00:03:53,740
It's the same premise.

46
00:03:54,780 --> 00:03:58,380
So the tag, in theory, could be used on the

47
00:04:00,040 --> 00:04:02,040
production side, but also at the retailer side.

48
00:04:03,040 --> 00:04:05,280
We also, I-- oh, Joe?

49
00:04:06,420 --> 00:04:15,580
Oh, well, the other thing was that we don't wanna just exclusively say you have to use a drone, because let's say you're a small nursery or you just wanna

50
00:04:15,620 --> 00:04:16,220
get a small

51
00:04:18,180 --> 00:04:19,140
block or something.

52
00:04:19,579 --> 00:04:21,480
You can just use a handheld reader.

53
00:04:22,200 --> 00:04:27,560
Uh, so it could be an either-or type, you know, situation.

54
00:04:27,660 --> 00:04:35,760
And we also extended it to a, uh, entry just into, uh, getting to work with this.

55
00:04:35,860 --> 00:04:38,740
One of the nurseries that we work at-- is with

56
00:04:40,200 --> 00:04:42,340
the corner of a block.

57
00:04:42,940 --> 00:04:51,190
And so as they drive in a vehicle down the road, they would just shoot a handheld gun out the truck window.

58
00:04:52,260 --> 00:04:59,260
And on their laptop, which is already there in their cab, they would look at the crop history so that this tag

59
00:05:01,240 --> 00:05:03,640
be directly linked to their inventory system.

60
00:05:03,710 --> 00:05:07,300
And they would know, "Hey, we fertilized that crop three months ago."

61
00:05:07,440 --> 00:05:09,060
I could see that on the screen.

62
00:05:09,600 --> 00:05:17,100
But I called up-- I made a link to our production database using an RFID tag.

63
00:05:17,960 --> 00:05:24,500
So that was kind of a, a lower, uh, step into this process.

64
00:05:24,540 --> 00:05:29,490
Instead of labeling every plant, we would just label, like, a block and then

65
00:05:31,160 --> 00:05:31,780
database.

66
00:05:32,000 --> 00:05:34,060
So even that's something.

67
00:05:34,080 --> 00:05:36,100
And then I wanna give credit.

68
00:05:36,560 --> 00:05:39,840
A big-- Joe is just exceptional in that, again,

69
00:05:41,800 --> 00:05:45,220
so he's always working on what he calls dashboards.

70
00:05:45,740 --> 00:05:47,320
And so the, the, you know,

71
00:05:48,720 --> 00:05:56,320
it's useless if we just collect this information, but we need to generate that, put it in some kind of a very graphically usable form.

72
00:05:57,040 --> 00:06:07,088
Ping Yu: So, you know, he's great at developing these-- so, James has described the full version of the RFID

73
00:06:07,148 --> 00:06:14,268
system, but, uh, from the technical side, what kind of RFID tags are you using?

74
00:06:14,988 --> 00:06:23,828
Uh, what are the read range that you are achieving with the drone-mounted reader versus a handheld?

75
00:06:24,868 --> 00:06:29,228
Uh, what does the data pipeline from those readers to the dashboard look like?

76
00:06:31,328 --> 00:06:31,548
Joe Maja: Yeah.

77
00:06:31,748 --> 00:06:33,678
So from a, uh,

78
00:06:35,088 --> 00:06:42,668
technical perspective, we're primarily using passive, not active-- passive UHF RFID tags.

79
00:06:42,688 --> 00:06:49,388
So these are cost-effective and don't require a power source, which makes them practical for large-scale deployment.

80
00:06:49,528 --> 00:06:57,968
So the read range depends on several factors, including the reader configuration, tag orientation, and environmental conditions.

81
00:06:58,048 --> 00:07:00,038
In fact, we have a publication for this.

82
00:07:00,748 --> 00:07:06,908
With a drone-mounted system, that directly influences flight planning, altitude, speed, and coverage pattern.

83
00:07:07,498 --> 00:07:11,788
So handheld readers provide more controlled scanning, but require manual operation.

84
00:07:12,548 --> 00:07:16,508
Now, the, uh, data pipeline is also important.

85
00:07:16,608 --> 00:07:24,288
So once the tag is read, that information is transmitted to a backend system where it's processed and visualized through a dashboard.

86
00:07:25,088 --> 00:07:29,248
Uh, that's what turns raw data into something actionable.

87
00:07:30,938 --> 00:07:41,288
Ping Yu: (upbeat music) One of the things that I wanna ask you guys is, like, what's the difference of, uh, like, a traditional normal tag or, uh, normal label

88
00:07:41,368 --> 00:07:46,248
versus an RFID label, and why growers

89
00:07:47,728 --> 00:07:52,408
need to use an RFID label instead of the traditional label?

90
00:07:52,468 --> 00:07:55,308
Well, you kind of touched base a little bit on there.

91
00:07:55,348 --> 00:08:05,348
But-- and also, if people want to decide using the RFID label, I know there are companies that they can work-- uh, they can

92
00:08:05,408 --> 00:08:06,028
work with.

93
00:08:06,188 --> 00:08:15,808
Do you have any suggestions on how they can, uh, properly use that in their nursery to help with managing their, uh, inventory?

94
00:08:17,888 --> 00:08:18,768
What, what would be...

95
00:08:20,588 --> 00:08:20,688
Yeah.

96
00:08:20,788 --> 00:08:25,658
Thank you, Joe, for bringing that up because that's one of the questions I wanna ask you.

97
00:08:25,718 --> 00:08:35,928
And if people want to do it, why they should do it, and how much of the price difference between the RFID tag versus the normal tag, because

98
00:08:36,268 --> 00:08:40,708
at the end of the day, they're gonna make a decision: "Okay, is this worth doing it?

99
00:08:40,888 --> 00:08:42,828
How much money that I'm putting in there?"

100
00:08:43,288 --> 00:08:44,727
and all that sort of things.

101
00:08:44,768 --> 00:08:44,888
Joe Maja: Yeah.

102
00:08:44,958 --> 00:08:47,628
Let me start with the simplest way to think about it.

103
00:08:47,668 --> 00:08:50,857
So traditional plant tags are essentially visual.

104
00:08:50,908 --> 00:08:52,658
They rely on line of sight.

105
00:08:53,448 --> 00:08:58,748
You or your staff have to physically see the tag, read it, or scan it one at a time.

106
00:08:58,908 --> 00:09:04,008
So that works, but it's labor-intensive and doesn't scale well, especially as operations get larger.

107
00:09:04,708 --> 00:09:07,828
Now, RFID changes that completely.

108
00:09:07,948 --> 00:09:10,328
So with RFID, you don't need line of sight.

109
00:09:10,968 --> 00:09:14,868
You can read tags wirelessly, in bulk, and almost instantly.

110
00:09:15,508 --> 00:09:21,448
That means instead of scanning one plant at a time, you can scan hundreds or even thousands of plants in a matter of seconds.

111
00:09:22,208 --> 00:09:29,628
And that shift from manual one-by-one interaction to automated bulk data capture is really the core value of RFID.

112
00:09:30,828 --> 00:09:34,108
Now, why should a grower consider switching?

113
00:09:34,148 --> 00:09:37,628
So it really comes down to efficiency, accuracy, and visibility.

114
00:09:37,648 --> 00:09:42,808
With RFID, you're not just tracking inventory, you're creating a live, dynamic system.

115
00:09:42,888 --> 00:09:48,868
You know where your plants are, how many you have, and how they're moving through your operation in near real time.

116
00:09:49,568 --> 00:09:52,128
That's something traditional tags simply cannot provide.

117
00:09:52,548 --> 00:09:55,148
So another important aspect is traceability.

118
00:09:55,908 --> 00:10:06,128
So as the industry moves more toward data-driven decision-making and supply chain transparency, RFID gives you a foundation for that, so you can track

119
00:10:06,208 --> 00:10:12,428
plants from propagation all the way to retail, and even integrate that information into a point-of-sale system.

120
00:10:12,928 --> 00:10:17,088
So it's not just about inventory, it's about connecting the entire life cycle.

121
00:10:18,148 --> 00:10:25,868
Now, in terms of getting started, I usually recommend that, rather than trying to implement everything at once, it's very important to start small.

122
00:10:26,508 --> 00:10:33,028
Maybe choose a single block or focus on higher-value crops where the return on investment is easier to justify.

123
00:10:33,728 --> 00:10:37,668
And that allows you to test the system, understand how it fits into your workflow,

124
00:10:38,908 --> 00:10:41,268
and identify any challenges before scaling out.

125
00:10:42,308 --> 00:10:47,148
Now, on the vendor side, there are a number of RFID providers and integrators out there.

126
00:10:48,908 --> 00:10:55,628
The key is to work with someone who understands ag applications, because this isn't the same as retail or logistics.

127
00:10:55,808 --> 00:10:57,428
So things like moisture,

128
00:10:59,188 --> 00:11:02,668
uh, plant density, and container materials can affect performance.

129
00:11:02,708 --> 00:11:06,068
So you want a solution that's been adapted for that environment.

130
00:11:07,008 --> 00:11:11,168
Now, uh, let's talk about cost, because that's always the big question, right?

131
00:11:11,488 --> 00:11:14,768
RFID tags are more expensive than traditional tags.

132
00:11:15,388 --> 00:11:16,248
There's no way around that.

133
00:11:16,328 --> 00:11:22,668
But the way I encourage growers to think about it is not just the upfront cost, but the overall system value.

134
00:11:22,728 --> 00:11:33,018
So when you factor in labor savings, uh, reduced errors, uh, improved inventory accuracy, and better decision-making, the return on investment can actually be quite compelling.

135
00:11:33,908 --> 00:11:37,688
And the other thing to keep in mind is that costs are coming down.

136
00:11:37,748 --> 00:11:43,928
So as the technology matures and adoption increases, we're seeing more affordable options and better performance.

137
00:11:43,988 --> 00:11:50,428
So what might have been cost-prohibitive five or ten years ago is becoming much more accessible today.

138
00:11:50,488 --> 00:12:00,888
So at the end of the day, RFID isn't just a replacement for a traditional tag, it's a shift toward a more automated, data-driven way of managing nursery

139
00:12:00,948 --> 00:12:01,888
operations.

140
00:12:04,832 --> 00:12:14,882
Ping Yu: So over the years, 'cause, you know, I know you guys have been working on the drone application for, well, 20, almost 20 years

141
00:12:15,032 --> 00:12:16,312
or over 20 years.

142
00:12:16,932 --> 00:12:17,392
Have you...

143
00:12:17,532 --> 00:12:27,282
What have you learned over the years in terms of what works with a commercial nursery on drone application and what doesn't work?

144
00:12:27,532 --> 00:12:30,112
I wouldn't say what doesn't work-- what can be improved?

145
00:12:32,312 --> 00:12:33,792
Uh, what, what...

146
00:12:34,592 --> 00:12:40,232
Now, over the years, with your experience working with drone application, what have you learned?

147
00:12:40,392 --> 00:12:42,652
Like, what works and what doesn't?

148
00:12:42,732 --> 00:12:42,912
Joe Maja: Yeah.

149
00:12:43,132 --> 00:12:52,392
So one of the biggest lessons I think we've learned over the years is that, uh, simplicity almost always wins, right?

150
00:12:53,032 --> 00:12:54,692
Simple is better.

151
00:12:55,532 --> 00:12:59,512
You can design a very sophisticated system in a lab or controlled environment, but

152
00:13:00,752 --> 00:13:05,492
if they're too complex to operate or maintain in the field, they're not going to be adopted.

153
00:13:06,532 --> 00:13:12,092
So the systems that work best are the ones that integrate smoothly into existing workflows.

154
00:13:12,712 --> 00:13:23,152
Growers already have established processes, and if a new technology requires them to completely change how they operate, it creates a problem.

155
00:13:23,412 --> 00:13:29,692
So we've learned to design systems that complement what they're already doing rather than replacing it entirely.

156
00:13:30,172 --> 00:13:35,672
Another important lesson is that agriculture is a very harsh environment for technology.

157
00:13:35,822 --> 00:13:40,852
You're dealing with dust, heat, humidity, wind, and sometimes unpredictable conditions, weather.

158
00:13:41,832 --> 00:13:48,792
What works perfectly indoors or in a test setting can fail quickly in the field if it's not designed with those conditions in mind.

159
00:13:48,872 --> 00:13:54,072
So we've also seen that reliability is often more important than precision.

160
00:13:54,632 --> 00:14:02,672
A system that works consistently, even if it's not perfect, is more valuable than one that's highly precise but unreliable.

161
00:14:02,972 --> 00:14:07,492
So growers need tools they can depend on day in and day out.

162
00:14:07,572 --> 00:14:14,151
So on the flip side, some of the things that don't work as well are over-engineered solutions.

163
00:14:14,192 --> 00:14:20,392
There's a tendency, especially from an engineering perspective, to try to solve every possible problem at once.

164
00:14:21,112 --> 00:14:27,832
But that can lead to systems that are difficult to use, difficult to maintain, and ultimately not practical.

165
00:14:28,592 --> 00:14:38,172
So over time, we've shifted toward more modular, scalable approaches-- start with a core functionality that delivers value, and then build on that as needed.

166
00:14:38,642 --> 00:14:41,632
And I think the final lesson is the importance of collaboration.

167
00:14:42,752 --> 00:14:52,092
So the most successful projects we've worked on have been the ones where engineers, horticulturists, and growers are all involved in the process.

168
00:14:52,132 --> 00:14:58,012
So that feedback loop is critical for developing solutions that actually work in the real world.

169
00:14:58,252 --> 00:15:04,052
Ping Yu: So Joe, I have seen, uh, increasing interest from growers in drones.

170
00:15:04,132 --> 00:15:11,912
Uh, over the past five years, has grower interest in drones grown, or shifted in what they're asking about, from your experience?

171
00:15:14,092 --> 00:15:14,412
Joe Maja: Yes.

172
00:15:14,532 --> 00:15:22,132
So we're, uh, definitely seeing the trend, and it's become much more noticeable over the past several years.

173
00:15:22,192 --> 00:15:28,072
So early on, when we would talk to growers about drones, most of the questions were very general.

174
00:15:28,132 --> 00:15:30,552
Things like, "What can this technology actually do?"

175
00:15:31,232 --> 00:15:34,252
Or, "Is this something that's practical for my operation?"

176
00:15:34,852 --> 00:15:38,312
So it was really more curiosity than anything else.

177
00:15:38,492 --> 00:15:41,412
Now, that conversation has changed quite a bit.

178
00:15:41,512 --> 00:15:44,512
The growers are coming in with much more specific questions.

179
00:15:45,192 --> 00:15:53,972
They're asking about how to implement the technology, what type of system fits their operation, how to handle data, and what kind of return they can expect.

180
00:15:54,072 --> 00:15:59,512
So it's moved from curiosity into a much more practical decision-making stage.

181
00:16:00,192 --> 00:16:02,972
Another big driver is labor availability.

182
00:16:03,002 --> 00:16:13,252
As labor becomes more limited and more expensive, growers are actively looking for ways to maintain productivity, and that's where drones

183
00:16:13,312 --> 00:16:16,052
and related technologies are becoming much more attractive.

184
00:16:16,132 --> 00:16:23,472
So overall, I would say, Ping, the interest has clearly shifted from "What is this?"

185
00:16:24,031 --> 00:16:25,552
to "How do I use this?"

186
00:16:25,772 --> 00:16:30,052
And that's usually a strong sign that the technology is moving toward mainstream adoption.

187
00:16:31,692 --> 00:16:41,692
Ping Yu: (upbeat music) And what are the resources that's available for, um, a commercial grower who wants to get more into

188
00:16:42,112 --> 00:16:47,912
the drone application or adopt more advanced technology into their operation?

189
00:16:48,362 --> 00:16:58,652
Are there any-- 'cause I know you guys have put out a lot of, like, extension fact sheets and, um, magazine, trade, uh, magazine papers out there, but are there

190
00:16:58,692 --> 00:17:03,672
any additional resources that's available for them to learn more about it?

191
00:17:05,652 --> 00:17:06,452
Mm.

192
00:17:06,652 --> 00:17:06,912
Yeah.

193
00:17:07,952 --> 00:17:09,792
Um, I don't...

194
00:17:09,932 --> 00:17:16,312
Well, I heard there might be some, like, a group of researchers who are doing drone, um,

195
00:17:17,812 --> 00:17:18,532
um, work.

196
00:17:18,992 --> 00:17:27,632
'Cause I think I came across a researcher from the University of Maine, and they have a group of people who are doing the drone work.

197
00:17:27,982 --> 00:17:33,731
And they have-- I think they have some sort of drone training program that you have to sign up for.

198
00:17:34,172 --> 00:17:38,032
But besides that, I don't have any other additional resources.

199
00:17:38,072 --> 00:17:41,952
And I think you have to pay for that too, but I could be wrong.

200
00:17:42,032 --> 00:17:46,332
Joe Maja: Well, a good starting point is university extension programs.

201
00:17:46,372 --> 00:17:56,732
So many land-grant institutions like, you know, UGA, Clemson, South Carolina State, and others are actively working on drone applications and

202
00:17:56,772 --> 00:17:58,952
publishing fact sheets, guides, and case studies.

203
00:17:59,052 --> 00:18:04,832
So these are usually free and designed specifically for growers, so they're very practical.

204
00:18:05,632 --> 00:18:08,768
Beyond that, there are also training programs.

205
00:18:08,828 --> 00:18:12,308
Some are short workshops, others are more structured courses.

206
00:18:12,348 --> 00:18:17,288
Some of these are offered through universities and others through private organizations.

207
00:18:17,358 --> 00:18:24,168
So they can be helpful, especially for understanding regulations like, you know, Part 107, and getting hands-on experience.

208
00:18:24,888 --> 00:18:30,428
Now, industry conferences-- uh, and I've experienced this one-- and trade shows are another valuable resource.

209
00:18:30,948 --> 00:18:37,788
They give growers a chance to see the technology in action, talk directly with vendors, and learn from other growers who are already using the system.

210
00:18:39,068 --> 00:18:45,307
For those who prefer a more self-directed approach, I think there's also a growing amount of content online.

211
00:18:45,428 --> 00:18:51,928
You can find video tutorials and forums, even on YouTube, where people share their experiences.

212
00:18:51,988 --> 00:18:55,288
So the key is to be selective and focus on credible sources.

213
00:18:56,088 --> 00:19:06,168
What I usually recommend is starting with extension materials to build the foundation and then moving into more hands-on training or vendor demonstrations once

214
00:19:06,228 --> 00:19:10,488
you have a clear idea of what you want to implement.

215
00:19:11,428 --> 00:19:22,007
Ping Yu: (on-hold music) And from, like, what I would like to know with your experience-- and, um, I know you guys are still looking for new

216
00:19:22,108 --> 00:19:32,128
ways to advance the technology or the drone work in nurseries-- what are some of the next steps that you're excited about in

217
00:19:32,208 --> 00:19:33,808
the drone work?

218
00:19:34,608 --> 00:19:41,828
Um, and what do you see this technology going forward being incorporated into the green industry?

219
00:19:42,348 --> 00:19:44,108
What is your vision for it?

220
00:19:44,208 --> 00:19:44,388
Joe Maja: Yeah.

221
00:19:44,528 --> 00:19:51,248
So looking ahead, what really excites me is the integration of multiple technologies into a unified system.

222
00:19:51,368 --> 00:19:58,708
So we're already seeing the pieces-- drones, RFID, robotics, AI-- but they're often used independently.

223
00:19:59,428 --> 00:20:04,708
So in the future, I think those systems will work together seamlessly, similar to what we're doing with Jim.

224
00:20:05,188 --> 00:20:08,448
They collaborated; technology collaborated as well.

225
00:20:08,478 --> 00:20:17,468
And for example, a drone could be used for monitoring crop health, an RFID system could track inventory in real time, and a ground robot could assist with transport or targeted

226
00:20:17,478 --> 00:20:18,117
application.

227
00:20:18,877 --> 00:20:24,748
All of that data would then feed into an AI-driven platform that helps growers make decisions.

228
00:20:24,808 --> 00:20:32,108
So instead of isolated tools, you have an interconnected system that supports the entire operation.

229
00:20:32,528 --> 00:20:36,688
I also think we'll see more automation in routine tasks.

230
00:20:36,708 --> 00:20:47,088
So things like inventory tracking, crop monitoring, and even certain types of application could become more automated, which is, um, especially

231
00:20:47,108 --> 00:20:50,528
important given the labor challenges the industry is facing.

232
00:20:51,308 --> 00:20:54,248
Another area of growth is in data analytics.

233
00:20:54,268 --> 00:21:00,898
So as we collect more data, the ability to interpret that data and turn it into actionable insights becomes critical.

234
00:21:01,868 --> 00:21:05,008
So that's where AI and machine learning will play a major role.

235
00:21:05,768 --> 00:21:08,808
But I think it's important to keep expectations realistic.

236
00:21:09,508 --> 00:21:18,328
You know, not everything will be fully automated overnight, and adoption will happen gradually, and it will vary depending on the size and type of operation.

237
00:21:19,068 --> 00:21:20,828
Still, the direction is clear.

238
00:21:21,288 --> 00:21:27,288
Now we're moving toward more connected, data-driven, and efficient systems in agriculture.

239
00:21:27,358 --> 00:21:35,088
Ping Yu: Do you think that in the next fifteen years or twenty years-- do you think that, uh, how many of those nurseries or commercial nurseries do you think they are

240
00:21:35,108 --> 00:21:37,528
gonna adopt, uh, this technology?

241
00:21:38,148 --> 00:21:39,548
Just give a, give a guess.

242
00:21:41,868 --> 00:21:42,448
Fifty percent?

243
00:21:42,568 --> 00:21:42,828
Joe Maja: Yeah.

244
00:21:42,888 --> 00:21:51,088
That's always a, uh, a challenging question, because adoption doesn't happen all at once or in the same way across the industry.

245
00:21:51,868 --> 00:22:00,728
But if I had to give a realistic estimate, I would say somewhere in the range of about fifty to seventy percent over the next fifteen to twenty years.

246
00:22:01,368 --> 00:22:05,128
That said, I think it's important to break that down a little bit.

247
00:22:05,188 --> 00:22:07,928
Not all applications will be adopted at the same rate.

248
00:22:08,048 --> 00:22:13,508
So for example, using drones for basic imaging or monitoring is already becoming quite common.

249
00:22:14,568 --> 00:22:21,768
And that type of application will likely reach higher adoption levels sooner, because the barrier to entry is relatively low.

250
00:22:22,288 --> 00:22:32,448
Now, on the other hand, more advanced applications like automated spraying systems, fully integrated RFID-based inventory tracking, or AI-driven

251
00:22:32,508 --> 00:22:41,948
decision support may take longer to scale, and those require more investment, more infrastructure, and sometimes a shift in how operations are managed.

252
00:22:42,028 --> 00:22:45,468
So another factor is the size and structure of the operation.

253
00:22:46,448 --> 00:22:56,568
Larger commercial nurseries are generally in a better position to adopt these technologies early, because they can justify the cost through efficiency gains and labor savings, right?

254
00:22:56,588 --> 00:23:02,648
The smaller operations may adopt more gradually or focus on specific use cases where the benefit is clear.

255
00:23:03,408 --> 00:23:12,488
So when I say fifty to seventy percent, I'm really thinking about a blended adoption across different technologies and different types of operations.

256
00:23:12,568 --> 00:23:17,888
Overall, I do think adoption will be significant, but it will happen in stages, not all at once.

257
00:23:17,988 --> 00:23:18,308
Ping Yu: Okay.

258
00:23:18,628 --> 00:23:18,908
Yeah.

259
00:23:18,988 --> 00:23:27,048
I, I think it's going to be, it, it has to be a game changer for-- to deal with the, uh, issue that we're facing right now with the

260
00:23:27,128 --> 00:23:30,328
labor and the safety issues and all, all in all.

261
00:23:30,948 --> 00:23:31,518
But, um,

262
00:23:33,588 --> 00:23:41,788
yeah, I think that's one of the beauties that I see from the work that you guys have done, because, uh, safety is one of the top issues that

263
00:23:41,848 --> 00:23:45,848
a lot of people, uh, at least in agriculture, are facing right now.

264
00:23:46,798 --> 00:23:55,558
(on-hold music) The last question that I wanna ask you guys: are there any other last comments that you wanna leave for the audience?

265
00:23:55,718 --> 00:24:05,768
Are there any suggestions you wanna give for people in horticulture in general, with your experience in horticulture

266
00:24:05,848 --> 00:24:07,208
for how many years?

267
00:24:09,452 --> 00:24:09,652
Joe Maja: Yeah.

268
00:24:09,972 --> 00:24:17,432
So I think one of the biggest takeaways from everything we've talked about is really the importance of collaboration.

269
00:24:17,552 --> 00:24:23,572
So this kind of work sits at the, um, intersection of multiple disciplines.

270
00:24:24,472 --> 00:24:31,072
Engineering, agriculture, data science-- and no single area can really solve these challenges on its own.

271
00:24:31,152 --> 00:24:38,292
So the most successful efforts we've been part of are the ones where those perspectives come together in a meaningful way.

272
00:24:38,352 --> 00:24:42,812
So for growers, um, I would say the key is to start with a clear objective.

273
00:24:43,572 --> 00:24:49,232
There's a lot of technology out there, and it can be overwhelming oftentimes.

274
00:24:49,712 --> 00:25:00,292
But instead of trying to adopt everything at once, it's much more effective to identify a specific problem-- whether it's inventory tracking, crop monitoring, or improving application efficiency--

275
00:25:01,272 --> 00:25:03,772
and then look at how these tools can help address that.

276
00:25:04,772 --> 00:25:08,572
It's also important to approach this with a mindset of gradual adoption.

277
00:25:08,612 --> 00:25:11,692
You don't have to transform your entire operation overnight.

278
00:25:11,872 --> 00:25:13,192
So start small.

279
00:25:14,072 --> 00:25:16,952
Test what works, learn from it, and then build from there.

280
00:25:17,092 --> 00:25:21,772
So that approach tends to lead to more sustainable and successful outcomes.

281
00:25:22,992 --> 00:25:28,652
And, uh, for students and young professionals, I would say this is really an exciting space to be in right now.

282
00:25:28,692 --> 00:25:32,072
Ag is evolving rapidly,

283
00:25:33,452 --> 00:25:43,742
and, uh, there's a growing need for people who can bridge technology and applied systems-- you know, whether your background is in engineering, uh, plant science

284
00:25:43,792 --> 00:25:47,632
or even data analytics, there are opportunities to contribute in meaningful ways.

285
00:25:48,452 --> 00:25:58,492
And for the industry as a whole, I think we're at a point where these technologies are moving from being experimental or research-focused into something that's becoming more

286
00:25:58,532 --> 00:26:08,542
practical and accessible-- they're not just concepts anymore, they're tools that can help address real challenges, especially around labor efficiency and

287
00:26:08,592 --> 00:26:10,692
sustainability.

288
00:26:11,752 --> 00:26:11,872
Yeah.

289
00:26:11,892 --> 00:26:15,992
Now, so I think overall, I'm very optimistic.

290
00:26:16,132 --> 00:26:25,332
Uh, there's a lot of work to be done, but the direction is clear, and I think we're going to see continued progress as these technologies become more integrated into

291
00:26:25,412 --> 00:26:26,362
everyday operations.

292
00:26:26,492 --> 00:26:34,832
Ping Yu: So Joe and James, with that in mind, if people want to find more about your work, where do you recommend they, uh, go look for more?

293
00:26:36,572 --> 00:26:45,622
I can put, I can put the resource or link, whatever, in there if you have any, like, source people can, can find more of your work.

294
00:26:47,792 --> 00:26:55,612
Yeah, 'cause I know Joe has a Google Scholar page, and then he also has a, like, a university profile page.

295
00:26:55,892 --> 00:27:00,612
Uh, I can, I can just easily put a link in there and put it in my show notes.

296
00:27:00,712 --> 00:27:09,752
Joe Maja: The easiest way to find my work is to search my name along, you know, with South Carolina State University or Google Scholar, which will take you to my university

297
00:27:09,812 --> 00:27:15,232
profile page, where you can see an overview of what we're working on.

298
00:27:15,292 --> 00:27:16,212
You can also find,

299
00:27:17,492 --> 00:27:24,572
yeah, many of my research publications, again, on Google Scholar, which is probably the most comprehensive place to follow our work over time.

300
00:27:25,392 --> 00:27:30,872
Um, we're also in the process of developing a center website that will highlight our projects and outreach activities.

301
00:27:30,932 --> 00:27:33,272
So that should be available soon as well.

302
00:27:33,392 --> 00:27:34,292
Ping Yu: Uh, thank you.

303
00:27:34,472 --> 00:27:38,852
Thank you so much, Joe. [laughs] Joe, Joe is-- Joe is recording from Japan.

304
00:27:39,552 --> 00:27:47,472
Uh, and thank you for joining me today, and, uh, it's, it's great to see you guys, and, uh, like I said, I really appreciate it, and you

305
00:27:47,532 --> 00:27:55,672
guys set a great example of how collaboration works and how collaboration matters.

306
00:27:56,061 --> 00:27:59,412
So thank you guys so much for joining me today.

307
00:27:59,452 --> 00:27:59,832
Thank you.

308
00:28:01,232 --> 00:28:03,112
That wraps up today's episode.

309
00:28:03,352 --> 00:28:04,252
How exciting.

310
00:28:04,352 --> 00:28:08,832
All those wonderful information-- that's, it's just incredible.

311
00:28:09,032 --> 00:28:19,272
So this is actually a very-- a great example of the, uh, future-shaping technology that is happening right now, uh,

312
00:28:19,392 --> 00:28:20,892
right in, in horticulture.

313
00:28:21,032 --> 00:28:31,372
So I thank Dr. James Robbins and, uh, Joe Maja for taking time out of their busy schedule, and Dr. Joe Maja is actually joining us from Japan.

314
00:28:31,452 --> 00:28:37,852
It's very late today, but I thank them for, uh, taking their time to talk with us.

315
00:28:38,292 --> 00:28:46,022
And, uh, always go check the show notes to learn more about this topic and other topics that we feature on this show.

316
00:28:46,652 --> 00:28:49,342
Uh, make sure you hit that subscription button.

317
00:28:49,712 --> 00:28:54,572
Consider giving a review for the podcast and donating to the podcast to support the show.

318
00:28:55,992 --> 00:28:58,572
Again, thank you for listening.

319
00:28:58,672 --> 00:29:02,332
Till the next time, stay healthy and go plants.

320
00:29:02,392 --> 00:29:02,932
Adios.

321
00:29:03,252 --> 00:29:11,172
(outro music)