AI and Small Farmers: Will Technology Reduce or Increase the Digital Farming Gap?

A farmer stands in a field looking at a crop that does not look quite right.

The leaves are changing colour. Rain has been irregular. Fertilizer is expensive. The local agricultural officer may not be available today. A trader is offering a price, but the farmer does not know whether waiting another week will help or hurt.

Now imagine that the farmer takes a photograph of the crop with a phone.

An AI system analyses the image, checks weather information, compares the crop with disease patterns, and suggests what may be happening.

That sounds like the future of farming.

But there is another farmer, perhaps only a few kilometres away, using an old phone with unreliable internet, little digital training and no money for sensors or subscription-based agricultural software.

For that farmer, the AI revolution may not have arrived at all.

And this creates a much bigger question than “Can AI improve farming?”

The central question: Who gets to benefit from intelligent farming?

If AI becomes one of the most powerful tools in agriculture, will it help small farmers compete with larger commercial farms?

Or will the farmers who already have better internet, better equipment, better data and more capital simply become even more productive?

That is the real digital farming gap.

The World Bank's recent work on AI in agriculture argues that small-scale producers, who produce roughly one-third of the world's food, can benefit from AI through better farm advice, pest detection, precision agriculture, weather information, market forecasting and financial services. But it also stresses that infrastructure, skills, governance and inclusion are necessary for those benefits to reach small producers.

Table of Contents

1. What AI can actually do for a small farmer

2. The hidden problem: access before intelligence

3. Why AI could widen the farming gap

4. The data problem nobody should ignore

5. What happens when AI gives the wrong advice?

6. A better model: AI as a farming assistant

7. The Small Farmer AI Inclusion Framework

8. What governments, companies and communities can do

9. What the next ten years could look like

10. Conclusion

What AI can actually do for a small farmer

AI does not have to mean an expensive autonomous tractor.

Some of its most useful applications can be surprisingly simple.

A farmer could use a mobile phone to receive weather-based advice, identify a possible crop disease from an image, receive irrigation recommendations, compare market prices, understand soil conditions or receive reminders about planting and harvesting.

The World Bank's 2026 AI agriculture report identifies applications ranging from pest detection and precision farming to soil monitoring, price forecasting, traceability, climate-risk assessment and alternative financial scoring.

This matters because small farmers often suffer from an information problem as much as a production problem.

A large agricultural business may employ agronomists, analysts and consultants.

A small farmer may have experience, observation and local knowledge — but limited access to specialist information.

AI can potentially put some of that knowledge within reach.

FAO has also developed digital agricultural services that can deliver agricultural information through smartphones and SMS, showing that digital farming does not necessarily require sophisticated machinery.

The hidden problem: access before intelligence

Here is the uncomfortable part.

An intelligent agricultural system is useless to a farmer who cannot reliably access it.

FAO has warned that rural communities and marginalized groups can be both the biggest potential beneficiaries of digitalization and the groups most likely to fall further behind. Barriers include connectivity, affordability, digital skills and unequal access to services.

The World Bank's 2025 analysis similarly identifies poor connectivity, unreliable electricity, expensive technology, lack of relevant data and limited digital literacy as major obstacles to AI adoption in developing-country agriculture.

So the digital farming gap is not simply:

“Who owns an AI machine?”

It is also:

“Who has electricity?”

“Who has a smartphone?”

“Who can afford data?”

“Who understands the interface?”

“Does the AI speak the farmer's language?”

“Does it understand the local crop, soil and climate?”

Those questions can determine whether AI becomes an equalizer or another advantage for people who are already ahead.

Why AI could widen the farming gap

Imagine two farmers.

Farmer A has reliable internet, modern equipment, digital records, soil sensors and access to an AI advisory service.

Farmer B has a basic smartphone and depends largely on personal experience.

If both receive equally accurate advice, the gap might begin to close.

But if the AI system requires expensive hardware, constant connectivity, English-language instructions and large amounts of farm data, Farmer A gains another advantage.

This is why “digital agriculture for everyone” cannot simply mean making technology available.

It means designing technology around the realities of the least-connected user.

FAO has specifically noted that some digital tools are poorly suited to smallholders because they can be expensive, unavailable in local languages or insufficiently relevant to farmers' circumstances.

The technology can be brilliant and still fail.

The data problem nobody should ignore

AI learns from data.

Agriculture produces enormous amounts of potentially useful data: weather, soil conditions, crop images, planting dates, yields, disease patterns, prices and farm-level information.

But another question follows:

Who controls that data?

If a farmer provides years of information about his land and production to a technology company, what happens to that information?

Can it be sold?

Can it be shared?

Can it influence credit decisions?

Can it be used to predict the farmer's willingness to sell?

Could a farmer lose bargaining power because other actors know more about his production than he does?

These are not imaginary philosophical questions. FAO has highlighted concerns around farmers' access to data, data sharing, privacy, ownership and unequal bargaining power in data-driven agriculture.

The future farmer may therefore need a new kind of literacy:

not only literacy in farming,

but literacy in data.

What happens when AI gives the wrong advice?

There is another danger.

AI can produce useful recommendations, but it is not the field itself.

A disease-detection model may misidentify a problem.

A weather prediction may change.

A recommendation trained on one region may perform poorly in another.

A farmer following bad advice can lose a crop, money or an entire season.

That is why agricultural AI should support human judgment rather than replace it.

The World Bank's 2025 discussion of AI for small-scale producers makes a similar point: AI needs reliable local data and appropriate adaptation rather than simply being imported as a finished solution.

A farmer should be able to ask:

“Why is the system recommending this?”

“What evidence is it using?”

“What should I check myself?”

“Who can I contact if the recommendation looks wrong?”

Those questions turn AI from an authority into an assistant.

A better model: AI as a farming assistant

The most useful future may not be “AI versus farmers.”

It may be:

AI + farmer + local agricultural knowledge.

AI can process enormous amounts of information quickly.

The farmer understands the field.

A local agronomist understands regional conditions.

A cooperative understands the community.

A government extension service understands public agricultural programs.

Each has a different form of intelligence.

The mistake would be to remove one and assume the machine can replace everything.

The Small Farmer AI Inclusion Framework

A practical model for inclusive agricultural AI can be remembered as five questions:

1. ACCESS

Can the farmer actually use the technology?

It should work with affordable phones, low bandwidth and, where possible, offline or SMS-based systems.

2. LOCALIZATION

Does the system understand the farmer's reality?

Advice should reflect local crops, climate, soil, farming practices and languages.

3. HUMAN CHECK

Can a farmer verify important recommendations?

AI should not become an unquestionable agricultural authority.

4. DATA RIGHTS

Does the farmer understand what happens to farm data?

Consent, transparency, privacy and fair benefit-sharing should be built into the system.

5. ECONOMIC VALUE

Does the technology actually improve the farmer's livelihood?

A sophisticated dashboard means little if it does not reduce costs, reduce losses, improve decisions, strengthen market access or increase resilience.

This framework is not an established scientific theory. It is a practical way to test whether an AI farming project is genuinely designed for small farmers.

What governments, companies and communities can do

The solution cannot be placed entirely on farmers.

Governments

Invest in rural connectivity, reliable electricity, agricultural extension and digital skills.

Public agricultural data should be made more accessible where appropriate, while protecting privacy and legitimate commercial interests.

Pakistan, for example, already has a strong reason to treat rural connectivity as agricultural infrastructure. The World Bank has documented how digital access can reduce information and transaction barriers for small-scale farmers, including evidence from Pakistan that cellphone access can improve coordination between farmers and traders.

Punjab is also currently receiving World Bank support aimed at expanding broadband access and digital services, demonstrating that connectivity itself is becoming part of development infrastructure.

Technology companies

Build for the farmer who has the least powerful device, not the richest customer.

Use local languages.

Explain recommendations.

Keep pricing realistic.

Give farmers meaningful control over their data.

Farmer organizations and cooperatives

Shared technology can be more realistic than individual ownership.

A cooperative might collectively access weather data, soil analysis, machinery platforms or agricultural AI services.

That can reduce the cost of entry.

Farmers

Technology should be tested like any other farming input.

Do not blindly follow an AI recommendation.

Compare it with field observations and trusted agricultural advice.

Start with a specific problem where technology can produce measurable value.

What the next ten years could look like

The future of AI farming may not be dominated by giant robots.

It may be much quieter.

A farmer receiving a voice message in his own language.

An AI system warning about unusual weather.

A photograph helping identify a possible disease.

A digital marketplace connecting a farmer to buyers.

A cooperative sharing a soil-monitoring service.

A local extension worker using AI to support hundreds of farmers instead of relying entirely on one-to-one visits.

This direction is already visible. The World Bank's 2026 World Development Report argues that developing countries do not need to build the world's largest AI models to benefit from AI. Instead, they can adapt existing tools to local languages, institutions and needs, including low-cost systems that work through text messages and voice calls.

That idea could be particularly important for agriculture.

The smartest farming technology may not be the most technologically impressive.

It may be the one a small farmer can actually afford, understand and trust.

Practical Solutions / Action Framework

For anyone developing or evaluating AI for small farmers, ask:

Problem: What real farming problem are we solving?

Access: Can a low-income farmer use it?

Language: Can the farmer understand it?

Local data: Does the system reflect local conditions?

Human judgment: Can a farmer challenge or verify the recommendation?

Data rights: Who controls the farmer's information?

Economic value: Does it save money, reduce losses, improve income or reduce risk?

Scale: Can the model work beyond wealthy farms?

Accountability: Who is responsible when the technology is wrong?

If several answers are missing, the project may be technologically impressive but socially incomplete.

Conclusion

AI could become one of the most useful agricultural tools of this century.

But technology does not automatically create equality.

A farmer cannot benefit from an intelligent system that he cannot afford, cannot connect to, cannot understand or cannot trust.

The real opportunity is therefore bigger than putting AI into farming.

It is about putting useful intelligence within the reach of people who have historically had the least access to expert information.

The future question is not simply, “How intelligent will agricultural AI become?”

It is:

“Will our agricultural systems become intelligent enough to include the farmer with the smallest field, the weakest connection and the fewest resources?”

If the answer is yes, AI can help narrow the farming gap.

If the answer is no, we may simply create a smarter technology — and a wider inequality.