AgTech Companies List: A Product Label, Not an Industry

There is no official agtech companies list, and there is no official definition of what would go in one. NAICS classifies by production process, so these firms sit under machinery manufacturing, software publishing and computer-systems design with no code that collects them. Building this list means defining the category yourself.

This guide sits in a series about specific industries and, like procurement and 3PL, it covers something that is not one. The difference matters commercially: if you are selling to agtech companies, you cannot buy the list, and if you are an agtech company selling to farms, the list you actually want is a different one entirely.

How many agtech companies are there?

Officially, unknown, and for a structural reason. NAICS classifies establishments according to similarity in the processes used to produce goods or services, which is a production logic, not a market logic. Relevant businesses therefore appear in separate categories including farm-machinery manufacturing, computer-systems design and software publishing.

The definitional problem sits underneath the counting problem. GAO states plainly that there is no single common definition of precision agriculture. USDA ARS describes it as observing, measuring and responding to within-field variability through crop management, which is a description of practice rather than a boundary around an industry.

So when a tracker tells you there are N thousand agtech companies, the number is a function of that tracker's inclusion rules. Ask what they counted before you use it.

The market that is actually countable: farms

If you sell agricultural technology, your buyers are enumerated even when your peers are not, and the adoption data is the useful part.

27% of US farms or ranches used precision-agriculture practices to manage crops or livestock, on GAO's 2024 reporting of USDA data. That headline understates concentration: in 2023, guidance autosteering was used by 52% of midsize farms and 70% of large-scale crop-producing farms.

Older technology-specific data shows how uneven adoption is by practice. In the 2019 data analysed by ERS, yield maps, soil maps and variable-rate technologies were used on only 5% to 25% of acreage, while automated guidance was used on well over 50% of acreage planted to corn, cotton, rice and other listed crops. Those figures describe 2019 and should be labelled as such, but the shape holds: hardware that reduces operator effort spread fast, and data-layer tools spread slowly.

Two constraints on reach. 79% of US farms had internet access in 2022, up from 75% in 2017, so roughly one in five operations is hard to reach or serve digitally. And the 2022 Census counted 1.9 million US farms and ranches, which is a count of potential agricultural customers, not of agtech companies.

How to build a list in a category with no register

1. Write your inclusion rule down first: which technologies, which part of the value chain, hardware or software, direct-to-farm or via dealers. GAO's point about definitions applies to you as much as to anyone else.
2. Source across the codes rather than within one. Farm-machinery manufacturing, software publishing, computer-systems design and agricultural support services each hold part of the population.
3. Use adoption data to prioritise the farm side, because size predicts adoption more reliably than geography or crop.
4. Treat trackers and startup databases as leads to verify, not as a source of record, since each carries its own boundary.
5. Say "unpublished" internally when it is true. A defensible small list beats a large one built on someone else's definition.

Defining a boundary and then assembling companies against it, rather than downloading a category that does not exist, is the specific problem Causo's agtech prospecting is built for.

Why generic databases miss agtech

The label is self-declared. A database returns whoever writes "agtech" on their website, which includes consultancies and excludes machinery firms that have sold precision equipment for twenty years.

Classification scatters the market by design, so no filter reassembles it.

The distinctions that decide fit, such as whether a company sells hardware, software or services, and whether it reaches farms directly or through dealers and co-ops, are not fields anywhere.

And on the farm side, the attributes that predict adoption (acreage, crop mix, equipment fleet) are absent from business databases entirely, which is the same problem covered in the farm and ranch guide and in B2B prospecting for founders.

Who decides, on both sides of this market

If you sell to agtech companies, the buyer is a founder, CRO or VP of sales at a venture-funded firm, and the sales cycle looks like any other B2B software sale.

If you are an agtech company selling to farms, the buyer is the operator, adoption is driven by whether the technology reduces labour or risk in a season, and the strongest evidence is the autosteering pattern: the tools that spread fastest were the ones that made a hard job measurably easier on day one, not the ones with the best analytics. Seasonality and capital cycles govern timing, and dealer relationships often govern access. If you are still choosing a vertical, how to find customers for your startup covers the sequencing.