AI Farms in Texas: Adoption Data and Projects by Region

AI farms in Texas are still rare. Only 14% of Texas farms and ranches used any precision agriculture practice in 2025, against 22% of US farms and 50% in North Dakota, according to the USDA NASS Technology Use report (August 2025). The AI projects with public details are mainly research-station work and grower trials run by Texas A&M AgriLife, Texas Tech and USDA-ARS.

This page is about artificial intelligence and precision tools used on farms and ranches, not the AI data centers being built in Texas. It is written for agtech founders, farm-tech startups and product teams who need to know where Texas growers already use data tools, which regions and problems are worth building for, and what a monitoring service has to save before a grower will pay for it. You get four charts from USDA surveys, a table of real projects by region, and a break-even calculator for irrigation monitoring.

What “AI farms in Texas” means in practice

There is no official list or map of AI farms. USDA does not count “AI” on farms. The closest national measure is the NASS question on precision agriculture practices, which covers GPS guidance, yield monitoring and soil mapping, variable-rate inputs, drones for scouting fields or watching livestock, electronic tagging, precision feeding and robotic milking. A farm that answers yes to any of these counts as a user.

So when people search for AI on Texas farms, they usually mean one of four things:

• Remote sensing plus models: drone or satellite images turned into crop maps, yield estimates or stress alerts.
• Sensor-driven irrigation: soil or plant sensors and software that decide when to water.
• Machine guidance: GPS auto-steer and section control on tractors, sprayers and planters.
• Livestock tech: GPS collars, electronic ear tags and virtual fences on ranches.

Each has a different buyer, a different price and a different region where it matters most. That is the map this page draws.

▶ How satellite NDVI maps show crop stress across a field before it is visible from the ground. · 4:09

How many Texas farms use precision and AI tools

Texas is a low-adoption state by share of farms. In the 2025 NASS survey, 14% of Texas farms used precision practices to manage crops or livestock, up from 13% in 2023. Neighbouring Oklahoma was at 22% and New Mexico at 17%. The big grain states of the northern Plains were far higher.

Chart

Farms using precision agriculture practices, selected states

Percent of all farms using precision practices for crops or livestock, 2025

One likely reason is the shape of Texas agriculture. Texas had 230,662 farms in the 2022 Census of Agriculture state profile, and 46% of them sold less than $2,500 of products that year. Livestock made up 77% of the state’s $32.2 billion in farm sales. Pasture covered about 88.5 million acres against 27.9 million acres of cropland. Many small cattle and hobby operations have little reason to buy yield monitors or variable-rate controllers, so the farm-count share stays low even where large row-crop farms use a lot of technology.

For a startup, this matters. A percentage of farms is not a percentage of acres or sales. A product built for large irrigated row-crop farms in the Panhandle addresses a much smaller number of buyers than the 229,000 farm operations NASS counted for 2025, but those buyers manage a large share of the cropland.

Adoption is uneven across crops

National data by crop show where the tools are used. USDA ERS reports that auto-steer and guidance systems were used on 64.5% of US cotton acres and 72.9% of sorghum acres in 2019, while drones, aircraft or satellite imagery were used on only 2.8% of cotton acres and 4.6% of sorghum acres that year, in Precision Agriculture in the Digital Era (USDA ERS, 2023). These are US figures, not Texas-only, but Texas is the largest cotton state, so they are a fair signal: guidance is common, imagery-based decisions are not. The ERS Amber Waves summary on auto-steer adds that guidance is used on more than half of US corn, soybean, winter wheat and cotton acres.

Chart

Precision practice use, 2023 vs 2025, selected states

Percent of all farms using precision practices for crops or livestock

Where Texas growers are online and how they connect

Any AI tool needs data to move between the field and the cloud. In the same NASS survey, 85% of Texas farms had internet access in 2025 (89% in 2023). Of those online, 76% used a cellular data plan, 49% broadband and 31% satellite internet. Smartphone use fell from 92% to 84% of Texas farms between the two surveys.

The bigger change is in buying behaviour. The share of Texas farms that bought farm inputs over the internet rose from 21% in 2023 to 51% in 2025, and the share that ran marketing activities online rose from 10% to 23%. For an agtech company, that means more growers will now sign up, pay and learn online, but the product must work on a phone over a cellular link, often with weak coverage at the field edge.

Chart

How Texas farms use digital tools, 2023 vs 2025

Percent of all Texas farms

The Texas map: real AI and precision projects by region

This is not a map of every AI farm. It lists named, public projects we could check, and the problem each one targets. Regions with no project in this list may still have private users; we only list what a public source describes.

Coastal Bend: satellite and drone digital twins for cotton

The digital agriculture team at the Texas A&M AgriLife Research and Extension Center at Corpus Christi is working with 11 commercial cotton producers in the Coastal Bend. According to AgriLife Today (April 2026), their tools combine drone and satellite data to estimate crop biomass and lint yield early in the season. Growers get a data hub with near-daily heat maps of canopy cover and canopy health, and later in the season advice on crop termination timing and yield. Planned features include irrigation needs and economic risk.

The same program has been building its drone platform for years. An earlier AgriLife report on digital agriculture (2021) says a drone can map a 100-acre field in about half an hour of flight and build 3D models of the plants. The work started with cotton funding and has spread to wheat and vegetables.

For startups, this is the clearest sign of what Texas cotton growers want from AI: not a dashboard of indices, but answers to specific, timed decisions such as when to apply a growth regulator or when to defoliate.

Southern High Plains: sensors that decide when to irrigate

The Ogallala supplies more water than any other Texas aquifer, and about 95% of the water pumped from it goes to irrigation (Texas Water Development Board). The Texas Water Development Board says about 95% of the groundwater pumped from it goes to irrigated agriculture, and water levels have fallen by more than 300 feet in some areas over 50 to 60 years. Statewide, the 2022 State Water Plan projects irrigation demand to fall from 9.4 million acre-feet a year in 2020 to about 7.6 million in 2070.

At Bushland, near Amarillo, USDA-ARS and AgriLife scientists tested an automated system that reads plant and soil sensors and schedules irrigation. In a two-year study published in Frontiers in Plant Science (2023), cotton watered at 75% of the full rate kept its seed cotton yield while using at least 20% less irrigation in 2021 and 16% less in 2022. The automated scheduling performed about the same as manual soil-water readings, which take far more labour. This is one site and two seasons, so treat it as a proof of concept, not a promise for every field.

Irrigation tools fit closely with drip and pivot automation; our guide to irrigation automation systems compares common setups.

South Plains and Blackland: drones and AI for cover crops

Around Lubbock, AgriLife scientists study dryland and deficit-irrigated cotton. An AgriLife researcher there is working with a digital agriculture specialist at the Blackland Research and Extension Center in Temple to use drones and AI to build a decision tool for when to end a cover crop, according to AgriLife Today (October 2025). Ending a cover crop too late uses soil water the cotton needs; ending it too early loses the soil benefit. That is a narrow, well-defined decision, which suits a model-based tool.

Panhandle rangeland: virtual fences for cattle

Texas had 12.1 million cattle and calves on 1 January 2026 (USDA NASS). Ranch tech gets less attention than crop tech, but it is where much of the state’s farm value sits. After the Smokehouse Creek fire burned more than one million acres across the Texas High Plains region in February 2024, a Texas Tech research team began a multiyear study of GPS virtual-fence collars on a cow-calf ranch based in Canadian, Texas, which lost 95% of its main ranch to the fire. The project has a $499,351 grant from the National Fish and Wildlife Foundation. The collars give a sound cue near a set boundary and a mild shock if an animal crosses it, so ranchers can move cattle without rebuilding cross fences.

How Texas irrigators decide when to water

Irrigation is the clearest test of how far data tools have spread. The 2023 Irrigation and Water Management Survey asked farms what they use to decide when to irrigate. Of 10,896 Texas farms that irrigated in 2023, 9,172 judged by the condition of the crop and 3,926 by the feel of the soil. Only 1,133 (about 10%) used a soil-moisture sensing device, and just 85 (under 1%) used computer simulation models. In Nebraska, 3,335 of 13,128 irrigating farms (about 25%) used soil-moisture sensors.

Chart

How Texas irrigators decide when to water

Number of Texas farms using each method (selected methods; farms can use more than one), 2023

Texas irrigators applied an average of 1.2 acre-feet of water per irrigated acre in 2023, on 3.72 million irrigated acres. With about 4.6 million acre-feet applied in 2023 and so few farms using sensors or models, irrigation scheduling is one of the largest open gaps for AI tools in the state.

What it costs and what it must save

A grower pays for a monitoring tool only if it saves more than it costs. For irrigation, the saving is usually pumping energy and water. The calculator below lets you test your own numbers: irrigated acres, water applied, your pumping cost per acre-foot, the share of water you think the tool could save, and the tool’s price.

For a startup, the most useful output is the last line: the highest per-acre fee a grower could pay and still break even. If your price is above that line for typical customers, the product needs to save more water, cut another cost, or serve a higher-value crop.

Try the numbers for your own farm below.

How to choose an AI or precision tool for a Texas operation

Whether you are a grower picking a tool or a startup deciding what to build, the same questions apply:

1. Start with one decision. The Texas projects above each answer one timed question: when to water, when to defoliate, when to end a cover crop, where to move cattle. Pick the decision first and the data second.
2. Check the connection. Three in four Texas farms online use cellular data. Tools that need constant broadband in the field will struggle.
3. Match the region. Water tools fit the High Plains; canopy and yield models fit cotton in the Coastal Bend and South Plains; collars and pasture tools fit rangeland.
4. Count the acres, not the farms. Farm counts and cropland acres give very different pictures. Size your market in acres or water volume.
5. Ask for local proof. Results from one research site, one season or another state may not carry over. Ask for trials on fields like yours.
6. Keep the data portable. Make sure field boundaries, maps and records can be exported to other farm software.

Satellite imagery is often the cheapest first layer because it needs no hardware in the field. Our review of tools for AI crop health and soil data compares common options.

▶ How satellite crop maps produce the zones that variable-rate equipment acts on. · 2:12

For agtech teams building on Texas farm data

In our view, a new standalone app is a hard sell to busy growers. An alternative is to add field data to a product growers already use: a seed or input dealer app, an irrigation controller, a lender’s portal or a cooperative’s grower system. Satellite-based crop and weather data can be pulled in through an API instead of being built from scratch; the Farmonaut agriculture API is one such option, and its satellite and weather API developer docs show how the data is delivered.

Field-level data also feeds services beyond agronomy. Examples include crop loan and insurance checks, product traceability records and carbon footprint estimates. Teams that manage many fields or field staff can look at the Agro Admin app, and growers can get the mobile app for phone access. Each of these depends on the same thing: accurate field boundaries and a steady record of crop condition over time.

Limits and open questions

• No single data source counts AI use. The NASS precision question lumps simple GPS guidance with advanced tools, and the 2025 national share fell from 27% to 22%, so changes between surveys need care.
• Research is ahead of farms. The named Texas AI projects on this page are research trials, some with a small number of growers. Commercial uptake is not yet measured.
• Water results are site-specific. The Bushland savings came from one site in two seasons and from deficit irrigation, not from sensors alone.
• Connectivity still limits real-time tools. Internet access on Texas farms dipped from 89% to 85% between 2023 and 2025.
• Small farms are hard to serve. With almost half of Texas farms selling under $2,500 a year, per-farm subscription prices must be low or aimed at larger operations.

▶ What farm technology means for rural towns: money, jobs and internet access. · 3:50

How we checked the numbers

We will refresh this Texas AI farm overview when NASS publishes its next technology survey. Adoption and internet figures come from the USDA NASS Technology Use report (August 2025). Irrigation decision methods come from the USDA NASS 2023 Irrigation and Water Management Survey, Table 25. Farm counts and sales come from the 2022 Census of Agriculture and the NASS 2025 state overview. Crop-level adoption comes from USDA ERS (2023). Project details come from Texas A&M AgriLife, Texas Tech and a peer-reviewed USDA-ARS study. We did not find a public list of “AI farms” in Texas, so we did not invent one; every place named on this page is a project described by its own institution.

We cover a connected topic in Agriculture in the Great Plains: Texas Innovations.

For a closer look at a related question, see our guide: Texas Agriculture Commissioner: 7 Ways to Advance Farming.

FAQ

Are there AI farms in Texas?

Yes, but few. In 2025, 14% of Texas farms used any precision agriculture practice (USDA NASS). Named AI projects include drone and satellite digital twins with 11 Coastal Bend cotton growers, sensor-scheduled irrigation at Bushland and virtual fencing on a Panhandle ranch.

Why is precision agriculture use lower in Texas than in other farm states?

Texas has many small farms and ranches: 46% sold under $2,500 of products in 2022, and livestock made up 77% of sales. Farm-count shares stay low even where large crop farms use guidance, sensors and imagery.

Which Texas region uses the most farm technology?

No survey ranks Texas regions. Public projects cluster on the High Plains (irrigation and cotton, around Bushland and Lubbock) and the Coastal Bend (cotton remote sensing around Corpus Christi), with rangeland tech in the Panhandle.

Does AI irrigation save water in Texas?

In one two-year USDA-ARS study at Bushland, cotton watered at 75% of full irrigation, scheduled by sensors or by hand, kept its yield and used at least 20% less water in 2021 and at least 16% less in 2022. Results on other fields will differ.

Is this page about AI data centers in Texas?

No. It covers artificial intelligence and precision tools used on Texas farms and ranches, not data center or server farm projects.

How can an agtech startup add satellite crop data to its product?

A common route is an API that returns field-level crop and weather data for a boundary, rather than processing satellite images themselves. Test it on fields in the region you plan to serve before you price the product.