The Algorithm and the Acre

 A Technology Arriving at Speed

In January 2025, John Deere took the stage at the Consumer Electronics Show in Las Vegas, the same venue where automakers unveil electric vehicles and tech companies debut wearables, and announced a suite of fully autonomous tractors, orchard sprayers, and commercial mowers capable of operating without a human driver.[1] The company’s 9 Series tractor, equipped with 16 cameras arranged in pods for 360-degree field visibility, high-precision GPS accurate to within an inch, and an AI system capable of detecting and responding to obstacles in real time, can till, plant, and spray an industrial-scale field while its operator monitors from a smartphone miles away.[2] John Deere aims to have fully autonomous corn and soybean systems commercially deployed by 2030.[3]

This is not a concept. It is a product. The company has been selling the first generation of autonomous tractors since 2022, and its See & Spray technology, which uses computer vision to distinguish individual weeds from crop plants and apply herbicide only where needed, treated more than one million acres in 2024, reducing herbicide use by an average of 59 percent and delivering measurable yield improvements alongside the chemical savings.[4] The tractor has always been the technology that changed farming most. The autonomous, AI-guided tractor may be doing so again.

The numbers behind this transformation are substantial and accelerating. The global AI in agriculture market was valued at $4.7 billion in 2024 and is projected to reach $49 billion by 2032, growing at more than 22 percent annually.[5] In 2024 alone, global investment in AI agriculture startups reached $6.3 billion, a 19 percent increase from the prior year, with venture capital accounting for 58 percent of that funding.[6] Governments in the United States, India, and the European Union collectively allocated more than $2.1 billion toward AI agriculture initiatives in the same year.[7] The capital flowing into this transformation is substantial, broadly shared, and shows no sign of slowing.

For the people who grow and eat food, this transformation raises questions that go beyond market size and investment returns. What does AI actually do on a farm? Who benefits, and who doesn’t? What does it optimize for, and what does that optimization leave out? And what, in the end, does it have nothing to say about?

What AI Is Actually Doing on Farms Today

Agricultural AI is not a single technology but a family of applications, each addressing a different challenge in the production of food at scale. Understanding them concretely,  what they do, how they work, and what problem they were designed to solve,  is the starting point for any honest assessment.

Precision Application: Doing More with Less

The most commercially mature AI applications in agriculture are centered on precision application, using machine vision, machine learning, and real-time data analysis to apply inputs exactly where they are needed, in exactly the right amount, rather than uniformly across an entire field. John Deere’s See & Spray is the best-known example, but the principle extends to fertilizer, water, and fungicide application as well. AI-driven irrigation systems analyze soil moisture sensors, weather forecasts, and crop water-demand models to deliver water precisely when and where plants need it, reducing consumption without compromising yield. AI-guided fertilizer application maps soil nutrient variation across a field and adjusts application rates in real time, reducing nutrient runoff while maintaining crop nutrition.

These precision applications represent genuine advances in resource efficiency. They reduce chemical use, water consumption, and input costs, benefits that are real for the environment and for farm economics alike. The caveats are equally real: the equipment is expensive, the data infrastructure required is substantial, and the benefits accrue primarily to large operations growing commodity crops at a scale that justifies the capital outlay. The minimum viable scale for many AI precision agriculture tools is measured in hundreds or thousands of acres, not the one to five acres that define most community farm and market garden operations.

Autonomous Machinery: Addressing Labor Through Technology

The autonomous tractor addresses a real problem. Agriculture faces a persistent and worsening labor shortage, driven by the aging of the farm workforce, restrictions on immigration, the physical difficulty of farm labor, and wages that have historically failed to compete with other sectors. The promise of autonomous machinery is that it can perform repetitive, physically demanding tasks, tillage, planting, spraying, mowing,  continuously and consistently, without the constraints of human endurance or the complexities of labor recruitment and management.

The technology is real, but the deployment is early. As of 2025, the total global fleet of truly autonomous John Deere tractors remains relatively small, and the equity research analyst community has noted that the agriculture industry is still “very, very early in this process.”[8] Autonomous systems currently perform best on large, flat, uniform fields, the geography of commodity crop agriculture. They are less suited to the variable terrain, diverse crops, and small plot sizes that characterize community farms and specialty crop operations. Robotic harvesting startups like Bonsai Robotics (focused on orchard nuts) and Four Growers (greenhouse tomatoes) are making progress on high-value specialty crops, but commercial scale remains limited and costs remain high.[9]

Predictive Analytics: Reading the Field Before the Problem Appears

Perhaps the most broadly applicable AI capability in agriculture is predictive analytics: using machine learning to analyze data from satellites, drones, soil sensors, and weather stations to forecast crop health, pest pressure, yield, and risk before the farmer can see these things directly. Platforms like IBM’s Agricultural Platform, Trimble’s suite of precision agriculture tools, and a growing number of smaller software companies provide farmers with dashboards that synthesize this data into actionable recommendations: which fields are showing early signs of moisture stress, where disease pressure is building, what the likely yield range is for the current season based on growth curves and weather models.

These tools are increasingly accessible to smaller operations as well. Climate FieldView, Granular, and similar platforms offer subscription-based access to predictive analytics at price points that community farms can consider. The gap between what large and small farms can access is narrowing, slowly. As of 2025, more than 60 percent of large farms have adopted some form of AI precision agriculture tool, while adoption among small and medium farms remains at 20 to 25 percent.[10] The technology is arriving; the distribution of access is uneven, and the gap is shaped by capital, connectivity, and the time required to learn new systems, all of which favor larger, better-resourced operations.

Disease and Pest Detection: AI in the Palm of Every Farmer’s Hand

One area where AI tools have genuinely reached small farms and individual growers is plant disease and pest diagnosis. Apps like Plantix, developed by PEAT GmbH and available free on smartphones, use image recognition trained on millions of photographs of diseased and healthy plants to diagnose crop problems from a single photo taken in the field. Plantix covers more than 30 crop types, achieves diagnostic accuracy above 90 percent in favorable conditions, and is available in multiple languages, explicitly designed for smallholder farmers in developing markets who do not have access to extension services or agronomists.[11] Agrio, Leaf Doctor, and PictureThis offer similar capabilities. These tools do not require expensive equipment, connectivity infrastructure, or technical expertise beyond what a smartphone provides. They represent AI applied specifically to the problem of democratizing agricultural knowledge,  and they work.

“AI in agriculture is not one thing. It is a spectrum, from $500,000 autonomous tractors optimized for 10,000-acre commodity fields to free smartphone apps that help a smallholder farmer in Kenya identify a fungal disease before it spreads. Both are real. Both matter.”

The Honest Concerns: Data, Access, and What Gets Optimized

Who Owns the Data the Farm Generates?

Every sensor, every drone pass, every AI-guided application generates data about the farm it is operating on,  soil health, yield variation, pest pressure, water usage, microclimate. That data is extraordinarily valuable, both to the farmer and to the technology companies, commodity traders, insurance providers, and agricultural lenders who can use it to price risk, inform purchasing decisions, and develop better AI models. The question of who owns that data, and what the technology company can do with it,  is one of the least resolved issues in agricultural AI.

As of 2024, the United States has no dedicated federal regulatory framework governing agricultural data privacy, unlike the healthcare sector (HIPAA) or financial sector (GLBA).[12] Many precision agriculture platforms retain broad rights to the data generated by their systems, even when that data concerns the most sensitive operational details of a farmer’s business. The Federal Trade Commission has investigated John Deere’s data practices, and farmer advocacy organizations have raised persistent concerns about data sharing with commodity traders and other third parties.[13] For small farms considering the adoption of AI tools, understanding what happens to their farm data is not a technical question, it is a business and sovereignty question, and one that deserves a careful answer before any platform is adopted.

The Access Gap: When AI Deepens Inequality

The benefits of agricultural AI, reduced input costs, improved yields, better risk management, are most fully available to the operations that were already best positioned to compete: large, well-capitalized farms with the land, connectivity, and technical staff to deploy and maintain complex systems. Small farms, beginning farmers, farms in rural areas with limited broadband access, and farms serving underserved communities face structural barriers to adoption that market forces alone will not resolve. More than 25 percent of U.S. farms in some rural areas lack the connectivity that AI precision agriculture tools require to function.[14]

The risk is not simply that small farms are left behind in an AI-driven productivity race. It is that AI further accelerates the consolidation that has already reduced the number of American farms by more than 8 percent between 2017 and 2024.[15] If the most consequential agricultural technologies are accessible primarily to large operations, and those technologies drive measurable productivity and cost advantages, the economic pressure on small farms does not diminish. It intensifies. The equity question in agricultural AI is not a side issue. It is central to whether this transformation produces a food system that is more resilient, diverse, and community-serving, or one that is simply more automated at the top.

What AI Optimizes For, and What That Leaves Out

Every AI system optimizes for what it is trained to maximize. Agricultural AI tools developed for commodity production are, almost universally, trained to maximize yield per acre, minimize input cost, and manage production risk within a monoculture or near-monoculture system. These are rational objectives for industrial agriculture. They are not the objectives of a neighborhood farm.

A community farm growing 40 varieties of vegetables, herbs, and fruits for a local market is not trying to maximize bushels per acre of a single commodity. It is trying to grow food that is nutritionally dense, ecologically diverse, appropriate to its specific soil and microclimate, genuinely delicious, and meaningful to the community it serves. The variables that matter, varietal selection, succession planting, soil biological health, community relationships, what the CSA members want to eat, are not variables that any currently available AI system captures or optimizes for. The farmer’s judgment, shaped by years of observation of that specific land and those specific community relationships, is doing something that no algorithm has been trained to do.

What the Algorithm Cannot Know

There is a useful distinction between data and knowledge, and between knowledge and wisdom. AI systems are extraordinarily good at processing data, identifying patterns in enormous datasets that no human analyst could parse, making predictions from satellite imagery that no scout could match in speed, and applying inputs at a precision that no human operator could sustain over hours of field work. These are genuine capabilities, and they produce genuine value in the right context.

What AI systems do not have is the kind of knowledge that comes from being in a specific place, over time, with full sensory attention. A farmer who has been growing on the same two acres for ten years knows things about that land that no sensor array captures: where the drainage runs poorly after a hard rain, which corner of the field warms earliest in spring, how the bees move through the plots on calm mornings, what the basil smells like when it is ready to harvest. This is not romantic mysticism. It is irreducibly local, embodied, accumulated knowledge that shapes hundreds of daily decisions, planting schedules, harvest timing, variety selection, market positioning, in ways that improve the farm’s performance year by year.

The relationship between a farmer and her community is similarly beyond algorithmic reach. The CSA member who tells the farmer about her mother’s recipe for the bitter greens that nobody else is growing. The restaurant chef who asks for the small, imperfect tomatoes that the farmers market won’t take because they make a better sauce. The neighbor child who comes by after school to watch the chickens. These relationships are the economic and social infrastructure of the neighborhood farm, and they are not data points. They are the texture of a community food system, generated by people who know and trust each other, and they produce a kind of value that no algorithm can generate, measure, or replace.

“What AI optimizes for and what a neighborhood farm optimizes for are fundamentally different things. One is trying to maximize yield per acre. The other is trying to grow food that a specific community of people can depend on, take pride in, and build their lives around.”

The Neighborhood Farm in the Age of AI

The transformation of industrial agriculture by artificial intelligence is real, consequential, and accelerating. It will reduce some input costs, improve some resource efficiency, and address some of the labor challenges that have pressed commodity farmers for decades. It will also, without deliberate intervention, concentrate the benefits of that transformation in the hands of the operations that are already largest, and deepen the structural disadvantages that community farms and small specialty producers already face.

This is not an argument against agricultural AI. Precision herbicide application that reduces chemical use by 59 percent is better for the environment than uniform broadcast application. Free smartphone apps that help a beginning farmer diagnose a fungal disease before it destroys her crop are better than no support at all. The tools that help small farms manage their CSA subscriptions, track their cash flow, optimize their planting schedules, and communicate with their communities are genuinely useful, and an increasing number of them are designed specifically for small operations, accessible on mobile devices, and priced for tight farm budgets. The appropriate response to AI in agriculture is not blanket rejection. It is discernment: adopting what genuinely serves the farm’s mission and the community it feeds, and being clear-eyed about what it cannot do.

What the NFUSA network represents, in the age of AI, is something that no algorithm has yet learned to produce: farms that are deeply known by the people who grow on them, embedded in communities that care about what they grow, managed by farmers whose knowledge of their specific land accumulates and deepens over years. That kind of farming is not less valuable because AI is arriving in the field next door. It may, in fact, be more valuable, precisely because the qualities that define it are the ones that the transformation of industrial agriculture cannot replicate.

The food system needs both: the scale efficiencies that AI can bring to the production of commodity staples, and the local, relational, ecologically intelligent farming that neighborhood farms practice. The question is whether the systems that shape agriculture, in policy, in investment, in research, will recognize and support both. At NFUSA, we are committed to ensuring that the answer is yes.