For farmers in drought-prone regions, every drop of water counts. But knowing exactly where and when to irrigate crops isn’t always easy, especially for smaller agricultural operations that may not have access to expensive monitoring technology. Two brothers from the University of Texas Rio Grande Valley (UTRGV) believe artificial intelligence could help change that.
I recently had the opportunity to interview Edgar Bello Gonzalez and Eduardo Bello Gonzalez, the winners of Zoho’s first-ever “From the Ground Up” AI Hackathon. Their winning project, Dos Ojos, is an AI-powered crop monitoring system designed to help farmers make smarter irrigation decisions using satellite imagery, drones, and simple text messages.
What I found particularly interesting about their story is that the inspiration behind Dos Ojos comes from personal experience. Growing up in Mexico, the brothers witnessed firsthand how severe water shortages affected their community, damaging crops, threatening livestock, and driving up food prices. After moving to Texas’s Rio Grande Valley, they began noticing similar environmental challenges facing the region’s agricultural community and decided to do something about it.
Their approach combines free Sentinel-2 satellite imagery with optional drone-based monitoring to identify crop stress and recommend where irrigation water may be needed most. Rather than requiring farmers to learn complicated software or navigate technical dashboards, Dos Ojos delivers recommendations through accessible, bilingual SMS messaging.
The brothers also bring complementary skills to the project. Edgar, a computer science major with a minor in electrical engineering, handles much of the technical development. Eduardo, an electrical engineering major with a minor in computer science, focuses on project planning, farmer outreach, and communicating the technology’s potential benefits.
Living in an agricultural community myself, I understand how important water management is to farming operations. What caught my attention about Dos Ojos is its potential to put technology that might otherwise seem out of reach into the hands of smaller farmers without requiring major investments or extensive technical knowledge.
During our conversation, I asked the brothers about their experiences growing up in Mexico, how those experiences inspired Dos Ojos, the technology powering their solution, and what they learned from testing it with local farmers. We also discussed their experience participating in Zoho’s hackathon and what comes next as they work to turn their award-winning prototype into a tool that farmers can use.
Here is my conversation with Edgar and Eduardo Bello Gonzalez about their journey, their award-winning technology, and their vision for helping farmers make better use of limited water resources:
Leland McFarland: Hello, my small business community. My name is Leland McFarland, and I am here with Edgar and Eduardo Bello Gonzalez, who are the winners of Zoho’s From the Ground Up AI Hackathon, and I got a few questions for them. Edgar, Eduardo, glad to have you on. First of all, congratulations on winning Zoho’s From the Ground Up AI Hackathon. Can each of you introduce yourselves and tell us a little bit about what you’re studying at the University of Texas Rio Grande Valley?
Edgar Bello Gonzalez: Yeah, absolutely, I’ll go first. So, my name is Edgar Bello Gonzalez, and at UTRGV, for right now, I’m studying major in computer science and a minor in electrical engineering.
Eduardo Bello Gonzalez: Okay, um, I’m Eduardo Bello Gonzalez. I’m also in UTRGV, but I’m doing electrical engineering with a minor in computer science, so I have it flipped.
Leland McFarland: Awesome. Yeah, I actually that’s my background. I went to Oregon State University. I got my major in computer science. I tried, I dabbled in electrical engineering and then I found out I’m not gonna make any more money, and so I was like, okay, I’ll just focus on computer science without the less hours. But awesome that you guys are going all the way through with it.
Edgar Bello Gonzalez: Thank you.
Eduardo Bello Gonzalez: Yeah, I mean we have fun with it.
Edgar Bello Gonzalez: Yeah. I always love to meet a fellow computer science.
Leland McFarland: Yeah, it’s you know, I love seeing it out in the, you know, out in the wild as well. All right, so you guys are brothers as well as teammates. What was it like working together on Dos Ojos? And how did you divide up the responsibilities between the two of you?
Eduardo Bello Gonzalez: Well, I mean, I guess, I mean, we’re really used to working together. We, like we’ve been together our whole lives. We’re brothers, we’re twins actually, we’re fraternal twins. So, I mean, I guess he’s like the most reliable teammate I’ve ever had, to be honest. So it just makes everything easier. And then, I mean, we were both like really committed to the project, so like we both knew we could account for each other doing their part. To be honest, Edgar did most of the, like, the technical part, most to not say all of it. Because, you know, he’s computer science. He’s one of the best people I know, like, doing computer science. Like he would, he would explain to me what he’s doing, and I, and I do understand it, but I could never do what he’s doing. So he was taking, like, responsibility of that, and I was doing more of, like, doing, like working on the presentation, working on the speech, learning how to sell our idea, contacting the farmers. So basically I was doing all of the logistics behind it, and also like trying to give feedback, ideas on how to make it better, what we can implement, and how to, like, make it grow even more while he was working on, like, the actual, the actual computer science side of it.
Leland McFarland: Nice. How about you, Edgar?
Edgar Bello Gonzalez: Yeah, well, what Eduardo said is pretty much it. The tasks were mainly divided into, like, the project, like the program itself, and everything else. So, I pretty much took in charge, was in charge of everything that had to do with the project. Also, I wanted to explain to Eduardo like how it works so that way, on the presentation, any of us could answer any questions that the judges would make. And, yeah, he’s, he’s like really, really good at organizing everything. Even like some of the judges told us that something that we, that we decided like what the budget was going to go for, or like what was like the plan that we had ahead, was like really good, really complete. And, and that was pretty much all Eduardo, because he took care of everything, pretty much almost everything else that was not the program itself.
Leland McFarland: Sounds like you guys had some real good synergy between the two of you. All right. So, you two grew up in Mexico and experienced periods when your city didn’t have any water. How did seeing that, the impact of the water shortages on farmers and local communities, influence the idea behind Dos Ojos?
Eduardo Bello Gonzalez: Actually, you can go ahead. I think this one’s this one’s yours.
Edgar Bello Gonzalez: Okay, okay. So, yeah, we did grow up in Mexico, and there was quite a while, like there was a really long time where we didn’t have a lot of water. Sometimes we had to to wait like at a certain time of the day to shower, because maybe like during most of the day there was no shower. There was only water like during the morning or maybe on the evening. And for, it was personally bad for us, because not having that much water is, it’s really bad, but not only for us, also for the market itself, because the crops were like dying, and that also meant that the cattle was dying. So, things were starting to get more expensive, because there was less things. So, it was just like a lot of things that that led to each other because there was like no water. So, when we got here, which is like really, really close to where we were living. Oh, yeah, to where we were living. It had the very, very similar conditions, because we actually live like 2 hours away from where we used to live. So, the conditions are pretty similar. And we were starting to see that the patterns were like kind of starting, like how we looked back on Mexico. So, we didn’t want that to happen where we live now like how it happened where we lived back in Mexico. So, we decided to take, like take matters into our hands and thought, okay, how can we, how can we try to solve this problem? And that’s when we, oh, my bad, and that’s when we came up with this idea that that utilizing the water the most efficient way will definitely help us like save the crops and therefore save the cattle as well. And also because here on the RGV, there’s a lot of crops, but like a lot, even more than where we used to live.
Eduardo Bello Gonzalez: I mean, so, I mean, I guess when we first looked at the problem, like, of course, it was like, oh, there’s lack of water, like maybe like sometimes we want to shower and we can’t, like stuff like that that are more of an inconvenience, because like back then we were in middle school, high school, so it was more of, yeah, like they were inconveniences, because Mexico was going through like a certain drought like 5 years ago, no, like 4 years ago. So, it was like, it was, it was an inconvenience, yeah. But then, like, of course, when we moved here, like he was saying, a lot of the, like, climate and droughts and all that were similar, but we were like, okay, of course we’re not going to solve the whole problem of saying, okay, now we’re going to save water and we’re going to get like all of it and make sure that there’s no droughts ever. So, of course we had to like focus our idea on something. So, I mean, as he was saying, like the RGV, it has a lot of growth, like of growers of crops, of like agriculture. So, it was kind of like the, like the best target audience that we could find in the valley. And then, like, it’s such a core activity both in Mexico and in the RGV, so it was like everything started with the, like the idea in general of rationing water. But of course, like, we wanted to go into something more specific, something that would target something more like specific for the valley. So, that’s why it kind of like shifted of course, going more into agriculture, growers, water distribution for crops, and all that.
Leland McFarland: Great. All right, so after moving to Rio Grand Valley, you began seeing, you know, as you said, you began seeing similar drought and watering conditions. Was there a particular moment after you moved that you realized that this was the problem that you wanted to solve? Was there something like, was that that light bulb moment?
Eduardo Bello Gonzalez: I mean, I guess it’s just like, because since the moment we moved, we realized that agriculture here was like way more important than we thought. So, like when we moved, we realized like all of the, like agricultural departments they have and like how important, like agriculture and growing is here. And we kept going back and forth, like we keep going back and forth to Mexico, we go every weekend. So, like the problem, maybe like of course right now it’s not as present as it was like that many years ago, because they’ve kind of been working on it, but sometimes there’s still the occasion in which, in which there’s no water, like in specific places. So, the issue is still permanent. So, it’s kind of like having, having that situation of, since we’re going back and forth, we keep seeing it, like both here and there. But now we have like this insight of, like we realized that it’s, this is like way too present here. And then, of course, like since we’re now here, like we keep seeing more news and we learn more about the place and we like go around the city. So, we realized those kinds of things. It’s more of a, like a combination of the, of the new environment combined with the, with like the present and current issue that we’ve noticed. Like at some point, they were just like, oh, they just keep running in our head until, like at some point, we were asked if we could help, how we’d do it, and it just kind of like popped out of thin air based on what we’ve seen like all this time.
Edgar Bello Gonzalez: Yeah, for me, I wouldn’t say that there was a specific moment. It was just something that has always been on the background since the time that we moved here. Every time that, well, almost all the time that we looked at the, at the grass, it looked kind of pale instead of green. And when I saw like the prompt of, of like Zoho Hackathon, it immediately came to my mind, like, yeah, I’ve seen a lot of dryness here on the RGV, like throughout all the time that I’ve been here for like the last 2 years. So, it’s not like I realized like there was, like it was going to happen the same as Mexico. It’s just like since the time I came here, I’ve seen that it’s been getting slightly drier and drier, and when I saw the topic of the hackathon, I thought, okay, this might be a good opportunity to do something about it.
Leland McFarland: Great. All right, so for someone who isn’t familiar with agricultural technology, can you explain what Dos Ojos does and how a farmer would actually use it?
Edgar Bello Gonzalez: Okay, yeah, absolutely. So, how Dos Ojos works is that, okay, so it is divided into three parts. It’s the satellite imagery, the drone imagery, and the and the SMS message, which is the way that the farmers contact the the actual program itself. So, I’ll start with the SMS, which I think is the simplest one. They can just send a message to a bot that we have that just gets answers and gives replies in order to get information from the from the farmers about their farm. And at some point, it asks you, it asks you to get like, okay, where is your farm located? And then it asks you where your farm is located. You can open it, you can send the bot a link on Google Maps, and then you can select the area of your of your crops. Okay, at that time, that’s when the satellite imagery comes in. And it uses free satellite data from a satellite that it’s from AWS, it’s called Sentinel-2. And it goes around the Earth, I would say like every 5 days or so. So, it just, whenever you ask for a specific part, it just fetches in real time the information from that farm, like the one that is closest to it, like let’s say the satellite passed your crops 2 days ago, it will just get the most recent one. Not only that, it also gets like a few days and months ago in order to compare it on like how it looks now to how it looks, or how it used to look before. And from the satellite, since it’s like really far, there’s not a lot of things that you can see, but the few things that you can see are like really important. The thing that you can see from like really, really far is how green it looks. And how green a farm looks tells you almost all you need to know about it. So, it takes that into account, and it can also compare it to previous months, previous years to see if it used to look greener or has it always been like this. And that’s pretty much what it takes. It takes into account how green it looks versus how green it’s been looking throughout the years. And then the satellite part, I mean, the drone part is the second eye, which is, I want to say, slightly more accurate because it’s closer to you and it’s just a few feet on the air. And this one can get information from like each specific crop that is planted. Like, using computer vision, it can count like all the crops that there are, and then like box them into each itself, and it can also flag them like they’re healthy, you should look out for them, or they’re like dead. It also does this by comparing like each crop to the ones next to it. But the only problem with this is that if all of them are like kind of dying, they would all look the same, and you might think that they’re healthy, but the satellite can tell you otherwise, because it can look like exactly how it looks and how it’s supposed to look. So, for the farmer’s point of view, you just have to give it like the satellite data, like where is it located and select it. And then, if you have the chance, because you can also select only satellite or satellite and drone, you do not need to have the drone to do it. You can use satellite only as well, and it will give you like a really good estimate as well. But, of course, it will be even more precise with the drone as well. And the farmers do not need to know at all how this works. They just need to know information about their farm, like what they’re growing, where is it located, and when did they irrigate it.
Eduardo Bello Gonzalez: I mean, so, I guess something we told a lot is that, I mean, of course the program is meant to be used with the satellite and the drone, but if like if anyone were to want to start just with the satellite, they’re completely free to just do it, try it out for a while, and then see how, like what kind of recommendations and information they get. And then, once they get used to it, they can try it and get a drone and gather pictures too. So, it’s kind of you don’t have to go all in since the beginning. You can start with one eye and then add the other one. And then, something else is that the program, it gives you recommendations for water usage, but it also tells you a lot of information about why. So, also, like what we used the program, like they just send a text to the, because we’re working on getting servers. For the moment, it can only run if he has the laptop open, but once we get the servers running, like they would just have to send an SMS message to like to a chatbot and then just tell them like, ‘Oh, my field is located here. I’m growing this. I watered it like this time ago,’ like just some basic information about the field, and then that would be pretty much it. The chatbot is going to tell them recommendations about how to look for the crops, and it will also give them access to a file of the background of why it’s giving them all of those recommendations. So, the farmer can choose if they only want the recommendations or if they want to see all of the background information, too. So, it gives a lot of freedom of how they want to use it, how involved they want to get with the usage of it. So, yeah.
Leland McFarland: Nice. All right, so where does AI come in all this whole process? How does it help identify crop stress and determine where irrigation water may need to be applied the most?
Edgar Bello Gonzalez: Okay, no, that’s a great question. So, AI specific, in general, there’s like three sections for AI. There’s for the SMS message, there’s an AI that is reading the comments, and then it also gets all the information from the satellite, all the information from the drone, and all the information from the farmer. And with all that information, it takes into account to make a recommendation for it. But also, something really important is that it’s, well, at least that part of the AI, it’s a language model. So, it can be like, sometimes it may be wrong and it thinks that it’s correct. But we solve this by, just before like sending the text, we make sure that it actually matches to the soil data related with the irrigation data. If it matches, then that means that the recommendation is like true and it’s not hallucinating. But if it’s not matching what it should be, what the numbers are saying, then we tell it to like redo it again, like it did something wrong. And for the other section of AI, there is t