agsight

Jan 2024 - present

updated 2025

Helping 50+ specialty crop farmers grow more equitably

A hand wrapped in a produce wreath holds a screen with Agsight's plot overview screen.

30-second synopsis

I built the first cost-effective, sensor-free spatial machine learning app to help specialty crop farmers, primarily in Central California and Texas, combat vegetation stresses via algorithms in 4 key areas: crop growth, threats, water use, and soil fertility. So far, I've helped 50+ farms across 2 countries implement Agsight, who are seeing an average yield improvement of 12%, a 36% decrease in crop stress, and a 98% accuracy rate compared to ground-truth sensors. During this process, I spoke with farmers one-on-one to personalize Agsight based on their unique crops and challenges.

As of 2025, I have worked with Farm8 to improve agricultural equity for 5 Seoul restaurants by using metro farms and machine learning to consolidate farm-to-table supply chains and helped 7 California specialty crop farmers overcome saltwater intrusion from the Salton Sea to increase their yield by ~9.5%. Additionally, I have helped advance local environmental justice and sustainability efforts in Frisco ISD and beyond through cross-campus initiatives (sustain + ability) and the Global Sustainability Scholars Program.

A special note from Joon (2025)

Agricultural technology (agtech) today operates on a double standard. While large, commercial farms have access to the tools, teams, infrastructure, and funding to optimize their yields, most family-owned and specialty crop growers are stuck with outdated tools or no tech at all, simply because they can't afford or access the same resources. A 4th-generation peach farmer in California might still rely on handwritten notes, intuition, and traditional irrigation methods, while a corporate almond farm next door uses drone imagery, moisture sensors, and satellite analytics. The result is a digital divide where small farms face steep learning curves, data that don't reflect their unique microclimates, and unaffordable tech that demands time, expertise, and infrastructure they don't have. This makes it much harder to predict pests, fight water scarcity, or respond to vegetation stress.

That's why I developed Agsight. As a student who's personally experienced how gaps in financial resources can hold people back, I wanted to create a solution that helps close this overlooked gap by bringing affordable, easy-to-use, and personalized insights to the farmers who need them most. There were nights I sat staring at my screen at 2am wondering why I was still up debugging some obscure edge case for crop growth under partial canopy cover. It was easy to feel buried in the details. But the more I kept at it, the more I started seeing pieces of myself in the people I was building for. I was balancing school, projects, and helping care for my dad, who taught 7 classes a day in a rural district and still came home grading papers well into the night. So when farmers told me they were running irrigation on 3 hours of sleep or squeezing in fieldwork between shifts, I could relate. That’s what kept me going. I wanted Agsight to matter to the people who reminded me of where I come from. And I'm so grateful I've been able to share that experience with people I care about.

JoonLee

farms impacted

~12%

improvement in yield

~36%

decrease in crop stress

~98%

as accurate as sensors

Auditing the current experience

Analyzing the problem

To uncover common problems, pain points, and industry gaps, I surveyed 150+ specialty crop farmers across the US using agtech over their daily challenges, motivations, resources, and habits. I distilled responses into 6 key threads.

A scatter plot shows responses for "What has been your return on investment for your agtech solution?" The graph shows an increasing linear trend between yield increase and the price of agtech solutions: As the price increases, yield also increases, but marginally.

Unclear ROI

Farmers were frustrated by the unclear ROI of agtech tools. Many found it challenging to justify the high costs when the outcomes were not immediately evident. One shared that after spending $5K on a precision farming system, their yield only increased by 1%.

Bar chart comparing agtech solution effectiveness across crop types, showing common staples as most supported, heirloom vegetables as moderately supported, and fruits as least effectively supported, with varying levels of perceived ineffectiveness and detriment.

Lack of personalized data

Existing tools' lack of location-specific personalization result in data that were not only ineffective but sometimes detrimental. 34% of surveyed farmers reported that agtech solutions frequently overlook the needs of specialty crop farmers, including those growing heirlooms or fruits.

A funnel chart shows the percentage of surveyed farmers who were able to successfully schedule a demo, acquire infrastructure, complete setup, training, and start using an agtech solution. Only 54% of those who scheduled a demo started using the agtech solution.

Frictional onboarding

Many farmers reported that onboarding for many agtech solutions is lengthy and complex because it takes several weeks to schedule demos, acquire infrastructure (notably sensors), and finish training sessions. 83% of farmers over 50 reported struggling with a steep learning curve.

A pie chart illustrates responses to the question "What is the primary challenge you face when using your current agtech solution?" with 43% citing irrelevant features, 31% hard to interpret data, 14% limited applicability, and 4% no major issues.

Outdated

Many farmers complained about data being presented in cluttered interfaces in a spreadsheet format, which made it challenging to quickly find specific details or parse through complex data. 43% of farmers using agtech software also reported that their current solution had too many features that were not relevant to their specific needs or crops.

A stacked bar chart shows responses to the the question "How often do you find that the data provided by your agtech solution lacks actionable insights?" across four categories: Weather monitoring, soil monitoring, crop health, and pest detection. The majority of farmers report "often" or "always".

Lack of actionable data

Farmers reported that most data provided by agtech solutions lacked actionable, farm-specific insights, which made it difficult for them to use in real-time decisions. The data was presented in a vacuum, not applied for recommendations.

Bar chart showing that most users spend $1K–$2K per year on agtech tools, followed by $251–$1K, $0–$250, $2K–$5K, and the fewest spending over $5K.

Cost-prohibitive

91% of surveyed farmers cited the high price of agtech solutions as a major barrier to adoption. Primarily, high upfront costs and recurring fees put them out of reach for small- to medium-sized farms that typically operate on tight margins, which disincentivizes them from using agtech.

Asking the right people the right questions

Researching my audience

I read up on industry trends, interviewed farmers, and analyzed competitors to spot market gaps and set benchmarks. By working closely with farmers, I learned what they truly want, how they consume content, and where existing tools fall short—including the moments that cause them to drop off.

interviews

153

survey responses

competitive audits

Which features do you use most often? Why?

What do you find frusturating?

What outcome do you want to achieve?

What do you like about other agtech?

Which features do you use most often? Why?

What do you find frusturating?

What outcome do you want to achieve?

What do you like about other agtech?

How do you diagnose diseases or pests?

Has agtech lacked data you needed?

What farming task takes the longest?

What issues do you face with irrigation?

How do you diagnose diseases or pests?

Has agtech lacked data you needed?

What farming task takes the longest?

What issues do you face with irrigation?

Interviews

I conducted 8 virtual interviews via Zoom® and chose to target Central CA specialty crop farmers of small- to medium-sized farms because they were generally most affected by the inequities posed by current agtech solutions.

User journeys

I mapped each user archetype to their journey on competitor platforms by tracking their goals and outcomes. This helped me spot pain points, uncover opportunities to improve, and identify which features to prioritize.

Competitive audits

I analyzed 15 top agtech tools by comparing features, user feedback, and gaps to find opportunities for innovation and differentiation. One key insight: the median competitor price was around $750/year.

A spreadsheet evaluates and categorizes the sentiments of App and Play Store reviews of Agsight's competitors.

Sentiment analysis

Farmers frequently express frustration with high costs, inaccurate data, and friction during onboarding, while others expressed delight on unique features like smoke taint analyses and a focus on small producers.

Defining my audience

I dove deep into the behavioral data of farmers to understand them better by defining 3 archetypes representative of specialty crop farmers and mapped them to their respective jobs-to-be-done.

An early-stage grower building a specialty crop farm with a sustainability mindset, interested in tech but limited by capital and unsure where to start.

Challenges

Overwhelmed by the number of agtech options, most of which seem geared toward large-scale operations.
Wants tools that grow with their business, but many products lock them into high upfront costs or complex infrastructure.

Needs

A flexible solution with a low barrier to entry: something affordable now, but capable of expanding.
Clear evidence of how insights improve yield, reduce waste, or drive ROI, without requiring sensors, drones, or complicated tech stacks.

A black farmer balancing a peach on her bucket hat.

A long-time specialty crop farmer managing a small, family-run operation, increasingly pressured to adopt agtech despite limited familiarity with it.

Challenges

Finds most agtech overwhelming due to overly technical UIs and jargon that don't align with how they’re used to working.
Struggles to justify investing in expensive hardware or subscriptions when the ROI isn’t immediately clear or measurable.

Needs

An intuitive solution that requires no new hardware and provides clear, actionable recommendations.
Support materials or onboarding for non-technical users—something they can learn without hours of training.

A farmer stands in his orchard with a grocery tote bag full of fresh produce.

A mid-size farm manager responsible for field operations and administrative decisions juggling production and labor with minimal support staff.

Challenges

Don’t have the time to navigate dashboards filled with irrelevant metrics for crops they don’t grow or conditions that don’t apply.
The steep learning curve and lack of crop-specific insights lead to underuse or abandonment within a few weeks.

Needs

A tool that delivers local, crop-specific recommendations in a clean format so they can make quick decisions on the field.
A platform that reduces their mental load, with minimal setup, no required hardware, and instant visibility into what matters most: irrigation, stresses, and yield.

An elderly farmer peers into his fields behind his tractor.

Synthesizing research

Insights & opportunities

I distilled my research findings into 7 key insights.

Eliminate the need for infrastructure

Smaller farms simply can’t afford to install dozens of sensors—yet that’s been the only way to get detailed insights. These sensors are pricey, fragile, and often don’t survive the season. Although sensor-free farming has been discussed in scientific literature, it’s never made it to the real world.

Here, I decided to do something radical: Opt for a sensor-optional solution.

Make KPIs actionable

Across interviews and surveys, I kept hearing the same thing: farmers could access plenty of metrics like evapotranspiration and pest pressure—but didn’t know what to do with them. Numbers were everywhere, but guidance was nowhere. The real need? Clear, actionable advice tailored to their fields, like “adjust irrigation by 20 minutes” or “apply soil treatment here.” Whoever bridges the gap between data and decisions with simple, trusted steps will earn farmers’ trust and adoption.

Make solutions affordable

In every interview, cost came up. One farmer told me she skipped trying an agtech tool because the subscription would eat into her fertilizer budget for the season. Another couldn’t afford repairs when their sensor system broke mid-harvest. For small- to mid-sized farms on razor-thin margins, this is normal. The opportunity? Build a solution that respects those limits—no hidden fees, no costly hardware, and clear value from day one.

Personalize to improve relevance

With 47% of the surveyed farmers citing the high cost of agtech as a major barrier to adoption and 55% showing a low willingness to pay due to lack of location-specific personalization, there's a lack of personalized solutions that adapt to individual farm conditions and crop types without extensive consultations.

Reduce friction dramatically

One farmer told me it took him 3 weeks just to schedule a demo, get his sensors delivered, and sit through a 2-hour onboarding call. Every extra step, login, or setup screen becomes a reason to walk away during harvest season when time is scarce. The opportunity is in building something that works out of the box—no hardware, no training manuals, no delays. The fewer the steps, the more farmers will use it—and see results.

Smoothen the learning curve

When I asked farmers what kept them from using the agtech tools they’d already paid for, most talked about feeling overwhelmed. One grower, 68, said he gave up on a dashboard after three logins because he couldn’t figure out how to set up his field zones. Another said the interface felt like “Excel threw up.” With 32% of the surveyed farmers over 65 and only 35% holding a college degree, the opportunity is in removing friction. That means zero-setup onboarding, interfaces that show only what’s needed, and agtech that doesn’t assume a background in data science by meeting farmers where they are instead of expecting them to catch up.

Automate manual tasks

Many farmers told me the same thing in different words: they’re exhausted by error-prone manual tasks — checking soil moisture, walking rows to spot early signs of stress, tracking pests on clipboards, or calculating irrigation needs. As such, the opportunity is in automation that flags what matters and frees farmers to focus on higher-level decisions.

Forming a business

From problems to solutions

I used this time to ideate multiple solutions to discover ideas quickly. My focus was to diverge first, converge later. Eventually, I landed at a SaaS business model offered as a cost-effective, sensor-free, spatial machine learning Android® app to help specialty crop farmers combat vegetation stresses, water scarcity, and soil infertility without sensors.

Data in a vacuum

Incompatible infrastructure

Steep learning curve

No local personalization

Reliance on manual labor

Unsustainable yield

Unaffordable solutions

Predicting pests + diseases

Focussing the solution

Drawing from user insights and ideation sessions, I brainstormed both must-have and bold feature ideas—each tied to six core experience pillars. With the vision and scope clear, I jumped into design and engineering.

Home

Fields overview
Yields
Notifications
Spatial agendas

Crop growth

Crop assessments
Personalized schedule
Phenology
Crop rotation

Crop threats

Crop stress detection
Disease diagnoses
Pest diagnoses + IPM
Climatic threats

Water use

Water needs
Water/salinity stress
Dynamic irrigation
Soil moisture

Soil fertility

Health + composition
Nutritional deficiency
Fertilizer use
AI-based composting

Other

IoT-based automation
Climate control
Nutrient dosing
Hydroponics support

Receiving some much needed feedback

Around this time, I had the opportunity to present my idea at the McFerrin Center for Entrepreneurship to deliver 2 presentations to faculty and industry professionals and secure seed funding as part of the 2024 Texas A&M Ideas Challenge, through which I acted on some (painful) but much needed feedback.

A collage showing a science fair-style project display titled "Agsight," handwritten notes with feedback on the project, and a worksheet outlining project resources, goals, and content.

What I learned from feedback

My approach tackled too many problems (agronomic, economic, geographic, etc.), which risked being too broad to effectively validate or implement. As such, I narrowed the focus to a single crop or issue within a defined geographic area, in this case by helping smallholders (orchards, vineyards, specialty crop farmers) primarily in central California improve their yield by combating vegetation stresses related to crop growth, water use, and soil fertility.

The final product

Meet agsight

Cost-effective, personalized, and scalable. Introducing the first sensor-free, high-accuracy, agricultural machine learning solution.

Learn more

A phone perched on a rock and leaning on an avocado showing a screen with tips and best practices for raising avocado. An avocado branch balances above the tip of the top left corner of the 25-degree tilted phone.

Optimize yield with spatial insights.

Save big on water: irrigation and stress.

Enhance soil fertility and nutrients.

Diagnose any disease or pest in seconds.

AI-mazing ways to sustain yield

When developing, my biggest priority was speed-to-decision. So I built Agsight's AI pipeline to process phenology, growth, and local climate data in tandem and suggest exact next steps to diagnose which disease or pest; understand where and why it was spreading; and recommend what to do now.

Diagnose and treat any plant disease or pest in seconds.

Threats

Track your crops' growth with beautifully precise data.

Phenology

Get instant updates so your crops get attention when they need it.

Health

Parse through location-specific information with ease.

Localization

The technical details

How I eliminated the need for sensors

Agsight uses an AI pipeline that combines satellite imagery, smartphone crop photos, and public climate data to simulate in-field sensing. Every few hours, it pulls multispectral imagery from Sentinel-2 to track plant health and vegetation indices. Farmers also snap and upload crop photos, which a convolutional neural network analyzes for signs of disease, pests, or nutritional issues. These visual data streams are combined with microclimate records from NOAA, soil texture / organic matter data from SoilGrids, and downscaled temperature grids from PRISM. A gradient-boosted ensemble model then estimates sub-surface / latent variables like volumetric soil moisture, pest pressure risk, and probability of pathogen spread. Unlike static sensors, Agsight learns each field’s / crop's baseline temporal patterns over 10–14 days and flags anything that deviates as a sign of stress.

Analyzing agronomic data

Agsight’s AI analyzes high-resolution satellite imagery to monitor phenological stages by detecting changes in leaf color, canopy density, and flowering patterns over time. It combines this with historical weather data and soil characteristics to model crop maturity, hydration status, and health through vegetation indices like NDVI and PRI. Using regression models trained on regional yield and productivity datasets, the system predicts expected yields and flags anomalies in growth or hydration that could impact yields.

Automating irrigation and climate control

Given the depth of personalization that Agsight's existing AI features had among our trial farms, I decided to extend them by allowing farmers who had access to IoT (e.g., sensors) to completely automate irrigation and climate control — both indoors and outdoors — more precisely than ever before.

Agsight works indoors, too.

By tracking factors such as temperature and humidity, Agsight helps optimize light conditions for each growth stage to reduce crop stress and lower the risk of disease.

A mobile app screen shows lighting data for Shelf B1 in an indoor farm, with hotspot markers on lettuce crops and a graph tracking chlorophyll concentration over time.

Irrigation, automated.

Agsight automates irrigation by using real-time soil moisture data to deliver precise, crop-specific amounts of water, personalized to growth stage and plant type.

A mobile app screen displays collected data from Row B in a vertical farm and shows live camera footage of lettuce and basil under grow lights, moisture sensor status, and irrigation controls.

Rich in nutrients, rich in sustainability.

Agsight supports hydroponic systems by tracking changes in water nutrient profiles and adjusting inputs based on the specific needs of each crop. This precision reduces reliance on chemical fertilizers and can increase yields by up to 25% in fast-growing greens like kale, basil, and arugula within weeks.

A mobile app screen displays a lettuce growth overview with a projected harvest time in late June and a line graph tracking electrical conductivity levels from April to August.

Ideal lighting conditions in seconds.

Agsight’s AI scanner maps light distribution across grow zones to detect underlit or overexposed areas in real time by recommending simple fixes like repositioning LEDs or adjusting intensity.

The technical details

Climate control

Agsight’s climate control feature continuously collects real-time sensor data like temperature, humidity, CO₂, and vapor pressure deficit to predict and maintain optimal grow conditions. A recurrent neural network trained on historical and live data forecasts the best settings several hours ahead. Every 30 seconds, it analyzes new readings alongside plant health signals like NDVI or chlorophyll fluorescence, and the model assigns confidence scores to possible actions—for example, a 75% chance that raising humidity by 4% and dropping temperature by 1.2°C will ease stress in early-flowering tomatoes. These insights adjust environmental setpoints, which are then translated into HVAC commands using a PID controller fine-tuned by reinforcement learning.

Irrigation

Agsight’s automated irrigation system uses root-level moisture sensors to send real-time data to an on-site edge device. A convolutional neural network trained on crop-specific water needs, light exposure, and soil type calculates the exact water dose each plant needs to stay healthy. For example, during peak basil growth, a quick drop in moisture might trigger a 14 mL pulse. The system also factors in weather forecasts and recent plant activity to avoid overwatering and prevent root stress.

Diagnoses, lightning fast

One of the most prominent uses of our machine learning models was to identify, classify, track, forecast, and manage plant diseases, pest infestations, vegetation stress, and pesticide use at the tap of a button. With an average accuracy of ~98%, it provides details on stress signals, personalized treatment, pesticide suggestions, and predicted development.

Powerful protection ahead of time

Farmers can assesses threats from air quality and climate to soil fertility and pests, right at their fingertips. Wildfires, frost, heat waves, pest threats — they can know ahead of time.

Agsight's notification screen showing the most urgent priorities, including a wildfire smoke taint risk.

The technical details

Diagnoses

Agsight uses the phone’s camera and a lightweight MobileNetV3 model to diagnose plant issues on the spot. When a grower snaps a leaf photo, the app enhances the image; isolates the leaf; and analyzes features like spots, discoloration, and pest residue. For example, a curled basil leaf with mosaic patterns might be flagged as cucumber mosaic virus with 96.7% confidence, based on a 400K+ image dataset. The app then highlights affected areas, estimates the stage of infection, and suggests treatment based on location-specific protocols. It also tracks past scans to monitor disease progression and maintain spray records.

Effortless irrigation

Many farmers told me they wasted time and water because they had no way of knowing which sections of their fields actually needed moisture as weather and crop needs changed week to week. So I designed a system where growers let Agsight interpret that data by analyzing seasonal patterns, crop type, and weather forecasts. Instead of sticking to static irrigation plans, farmers can now get real-time, field-specific recommendations.

A mobile app screen displays a water assessment for apple crops, showing a recommended irrigation level of 1.1 inches per week and a disease alert warning about apple scab.

Apply water where your crops most need it.

Water needs

Know where, when, and how to irrigate your crops and plots.

Irrigation

The technical rundown

Water needs

Agsight estimates each plot’s water requirements by analyzing multispectral satellite imagery combined with microclimatic data (among them solar radiation, temperature, humidity, and wind speed). The system uses vegetation indices (including NDVI and CWSI), which are calculated from thermal / near-infrared bands to map water stress across the field. These indices feed into a crop-specific water demand model that incorporates growth stage and phenological data from time-series image analysis. By comparing current water stress signals against historical baseline conditions for each zone, the AI predicts irrigation needs with spatial granularity down to 1-meter resolution.

Dynamic irrigation

Agsight uses the data from this process to guage water needs to create a georeferenced irrigation prescription map that can interface with variable-rate irrigation systems to target only the stressed subzones by activating specific valves and emitters corresponding to areas showing the highest evapotranspiration deficits and stress signatures. This feature is in beta.

High-quality soil results, in seconds

When I asked growers how they decide where to fertilize or whether their compost is “ready,” the answers ranged from “I eyeball it” to “I wait two weeks for a lab report and hope it’s still relevant.” That gap drove me to build a workflow where a single photo of soil becomes a full nutrient and texture profile within minutes.

Personalized schedules

I built Agsight’s task engine to cross-reference crop health, weather forecasts, and phenology, then create a prioritized daily to-do list (delivered as a notification and spatial map) so farmers always know what to act on first, and where.

Three mobile app screens display a farming agenda interface showing daily task suggestions, wildfire smoke risk alerts, and a satellite map with prioritized plot-specific schedules.

The technical details

Soil

Agsight assesses soil fertility by combining multispectral satellite imagery with past yield maps and, if available, lab test results. Its machine learning model links spectral patterns (like those tied to organic matter and mineral content) with key fertility indicators such as nitrogen, phosphorus, and potassium. By tracking changes over time, it maps nutrient levels across each plot and matches them to crop-specific needs by growth stage. The result is clear, geospatial maps showing where to apply fertilizer only where it’s needed.

Personalized schedules

Agsight builds a smart daily task list by combining real-time crop data— like growth, pests, and soil health with weather forecasts. Its AI ranks tasks based on crop stage, stress severity, and ideal weather windows. For example, if powdery mildew is likely and the day is dry, it prioritizes spraying. If soil moisture is fine and rain is coming, it skips irrigation. The agenda adapts to user goals (like maximizing yield or cutting chemical use) and factors in labor availability by delivering a time-stamped, geo-tagged checklist that keeps growers focused on what matters most.

Impacting farms, impacting people

Deployment

After I developed my MVP, I initiated outreach to 75+ farms, 8 of which initially agreed to trial Agsight. Currently, I've expanded this pilot program to 50+ orchards, vineyards, and other specialty crop farms, primarily in California and Texas.

Our impact

Website

I focused on clarity and trust by making sure farmers could immediately understand what Agsight does and why it matters to their daily work.

A black and white photo shows a window with curtains and a small balcony adorned with potted plants and flowers, partially framed by a leafy tree.

#urbanag (agsight x Farm8)

Improving agricultural equity for local businesses in Seoul by using metro farms and machine learning to consolidate commodity chains between farms and restaurants.

A hand uses a gold kitchen sprayer to rinse fresh vegetables including kale, tomatoes, and lemons in a marble sink.

Helping 7 CA farms combat salinization

Helping 7 California specialty crop farmers overcome saltwater intrusion from the Salton Sea to increase their yield by an average of ~9.5%.

Joon stands alone on stage under a spotlight in front of closed blue curtains, presenting to an audience in a dimly lit auditorium.

Presentations + conferences

In the audience: Frisco ISD Superintendent Dr. Mike Waldrip and the School Board, and DFW industry professionals in an April 2024 symposium at Frisco High School to advocate for improved agricultural education in high schools.

Alli Erggelet

Co-owner, Urban Edge Farm

"Agsight has been amazing. It's super easy to use, intuitive, and pulls in more data than we even thought was possible. From soil moisture to carbon flux, real-time nutrient and pH data, and everything in between. It's been a great partnership."

15%

increase in yield

18%

less water used

Ronnie Vance

Co-owner, Temalpakh Farm

"At Temalpakh, fighting soil salinity is important given our proximity to the Salton Sea. We rely on natural methods like leaching and cover cropping to enrich our soil, and Agsight has provided the insights necessary for us adapt these techniques more effectively."

17%

increase in yield

24%

less saline

Silvia Jackson

Owner, Brodasi Organic Farms

“Since using Agsight, I’ve cut my water use by almost 20% and my strawberries are looking healthier than last season. It even caught a salt issue in one of my lower plots before I saw any damage. Honestly, it feels like having a second brain that actually understands my fields.”

20%

less water used

13%

increase in yields

Kirk Lokka

Former manager, Emeritus Vineyards

"Last year, we faced a fire 10 miles away during harvest, and we had no way of knowing if the smoke would affect our grapes. When Agsight reached out with their smoke taint analysis, it was a relief because it helped us map out the terrain and weather patterns."

15%

increase in yield

18%

less water used

Domenick Bianco

Manager, Anthony Vineyards

“What surprised me was how different two neighboring blocks could behave once Agsight took over — one bounced back fast, the other needed salinity correction I would've missed...I never [imagined such a] difference could exist in the microclimate of one block."

35%

less saline

12%

less water used

Chef Park Chan-il

Restaurant manager, Gwanghwamun Ichungjib

"I used to rely on trucked-in greens...thanks to the metro farm, though, I walk down the street each morning to pick up [harvests] an hour earlier...Since switching, I’ve cut produce costs by 18%, and my customers constantly ask why our salads suddenly taste 'brighter'."

18%

decrease in costs

21%

less food waste

Empowering students in DFW and beyond

sustain + ability

I used Agsight to promote local environmental justice and agricultural sustainability efforts in Frisco ISD and beyond through cross-campus initiatives and the Global Sustainability Scholars Program.

Learn more

Our mission

A collage of students engaging in various educational activities, including receiving awards at a science fair, collecting data in nature, and using a phone with a tripod in an open field.

Researching for social + environmental good.

I currently help Frisco ISD students conduct environmental research projects and compete in ISEF / affiliated fairs, from analyzing the impact of urban agriculture on atmospheric pollution to investigating the impact of particulate matter on pediatric asthma vulnerability.

A collage of black-and-white images showing a virtual meeting with multiple participants, a close-up of a girl speaking, and a girl smiling while sitting outdoors near a fence.

Global sustainability scholars program

This fully remote, free, 8-week fellowship brings together a close-knit community of 150+ students annually from across the globe to explore new approaches to sustainability by conducting research, initating projects, and exchange ideas on sustainability issues.

Reflections

What I learned

Be authentic

I reached out to 75+ farms but heard back from only 15—and just 8 agreed to chat. I started wondering if Agsight was actually helpful or just noise. Then I realized the problem wasn’t the product—it was how I was reaching out. My emails sounded too polished, like a pitch, not a conversation. So I switched gears: I wrote like someone who genuinely cared. I mentioned their crops, the water challenges they’d faced, and shared screenshots instead of links. That’s when the responses finally came.

Expect the unexpected

Agsight has taught me to expect curveballs—like when a satellite tile turned every orchard neon purple, which threw off my disease model, or when a grower’s wifi dropped mid-onboarding, and we struggled through spotty cell service in a pickup truck. These glitches forced me to slow down and adapt by building failsafes for weird data, creating low-bandwidth SMS alerts, and triple-checking edge cases. Farming, tech, and weather rarely hand in hand—and that means designing for the unexpected.

Community is everything

I didn’t realize how much I valued mutual support until building Agsight. I thought I was just making a tool—but I ended up building relationships. Farmers sent voice memos from the field. Students who stayed after school to help me debug even when they had practice or family responsibilities at home. Mentors and peers who challenged my assumptions, told me when my UI didn’t make sense, and stayed on Zoom calls until midnight helping me figure out why a field wasn’t syncing. These were people showing up for each other, in small, consistent ways. And somewhere along the way, I stopped seeing Agsight as a tech project. Instead, it became a shared effort to make things just a little more possible for people who are constantly asked to do more with less.

Know your why

There were nights I sat staring at my screen at 2am wondering why I was still up debugging some obscure edge case for crop growth under partial canopy cover. It was easy to feel buried in the details. But the more I kept at it, the more I started seeing pieces of myself in the people I was building for. I was balancing school, projects, and helping care for my dad, who taught 7 classes a day in a rural district and still came home grading papers well into the night. So when farmers told me they were running irrigation on 3 hours of sleep or squeezing in fieldwork between shifts, I could relate. That’s what kept me going. I wanted Agsight to matter to the people who reminded me of where I come from. And I'm so grateful I've been able to share that experience with people I care about.

More work

About me

A collage of images showing different stages of assembling an Arduino-powered robotic vehicle with electronic components, wiring, gears, and a pinwheel on grassy terrain.

Inventing a rover-AI system to make clean air accessible for 2.4K+ Dallas students

atmospheric research

May 2024 - present