AI in Pakistan's Agriculture: Empowering Farmers

Discover how AI can revolutionize agriculture in Pakistan by merging traditional farming wisdom with advanced technology. Learn about the importance of farmer training, reliable data, and local-language platforms to enhance productivity and sustainability in farming.

POLICY BRIEFS

Nadeem Riyaz

9/14/2026

For generations, Pakistani farmers have planted by memory. They knew which week wheat should go into the ground, how many turns of irrigation a field would need, and what a certain cloud, wind, or insect might mean for the weeks ahead. This knowledge was rarely written in books or stored in databases. It was passed from father to son and mother to daughter, refined through decades of observing the same land, the same crops, and the changing seasons.

That knowledge still matters. In fact, it remains one of the most valuable assets of rural Pakistan. Farmers understand their soils, water sources, local weather patterns, and crop behavior in ways that no machine can fully replace. But the seasons on which much of this knowledge was built are changing rapidly.

Rain now arrives when it is least expected or does not arrive at all. Summers are becoming hotter, winters are becoming shorter, and water availability is increasingly uncertain. Pests and diseases can appear earlier, spread faster, and affect areas where farmers have not traditionally encountered them. Extreme weather can turn a promising crop into a serious loss within days. The old saying that “experience is the best teacher” assumes that tomorrow will look something like yesterday. In Pakistan’s fields, that assumption is becoming increasingly difficult to defend.

This is where artificial intelligence (AI) enters the picture not as a futuristic fantasy of robot tractors moving across enormous farms, but as a practical decision-support tool for farmers. AI can help analyze weather forecasts, satellite images, soil conditions, crop growth, irrigation needs, pest risks, and market information. Instead of relying only on what happened last season, farmers can increasingly combine their own experience with information generated from thousands of observations.

The goal is not to replace the farmer’s knowledge, but to strengthen it. A farmer who has spent decades reading the land can use AI to read it in new ways. The future of Pakistani agriculture may therefore depend not on choosing between traditional wisdom and modern technology, but on bringing the two together.

What AI Actually Does for a Farmer

Artificial intelligence, at its core, is a way of finding patterns in large amounts of information and turning those patterns into useful predictions or recommendations. For agriculture, that information can come from many sources: weather forecasts, satellite images, soil sensors, farm records, crop histories, irrigation schedules, and market prices. An individual farmer cannot possibly process all of this information at once. A computer can, and AI can help turn it into information that supports better decisions.

Consider water. A farmer may know from years of experience that a field usually needs irrigation during a particular week. But when temperatures rise, rainfall patterns shift, and water availability becomes uncertain, following the same routine can waste precious water or leave crops stressed. AI can combine weather forecasts, soil-moisture information, crop growth stages, and historical data to estimate when irrigation is actually needed and, where reliable data are available, how much water may be required. Across large canal command areas, even small improvements in irrigation efficiency could translate into substantial water savings.

Crop health is another area where AI can make a difference. Satellite imagery and drone photographs can detect subtle changes in plant color, growth, or density that may not be visible to the human eye until damage has already become serious. AI systems can identify these early warning signs and help flag possible pest attacks, diseases, nutrient deficiencies, or water stress. Within the same field, this technology can reveal areas requiring attention while showing where fertilizer or pesticides may not be necessary. This can reduce input costs and limit unnecessary chemical use and runoff.

AI can also help farmers manage uncertainty. Improved estimates of weather conditions, pest risks, crop yields, water availability, and even market trends can give farmers more time to respond. They may delay planting, select a different crop, adjust irrigation, protect livestock, or seek alternative markets before a problem becomes a major loss.

Lessons From Other Countries

AI is already being used in agriculture around the world, though in different ways. In parts of Africa, AI-powered advisory services are helping extend agricultural expertise to smallholder farmers who are difficult to reach through traditional extension systems. In the United States, satellite imagery and machine learning are used to improve national crop and yield estimates. In India, AI is increasingly supporting farmers with weather information and pest surveillance.

The applications differ, but the lesson is the same: the value of AI lies not in the technology itself, but in its ability to turn information into better decisions. Pakistan should not simply copy what others are doing. It should identify where better information can produce the greatest economic benefit for its own farmers, and start there.

Lessons from Global Agricultural AI

Artificial intelligence is already being used in agriculture around the world, although its applications vary according to local needs, farming systems, infrastructure, and access to technology. In parts of Africa, AI-powered advisory services are helping extend agricultural knowledge to smallholder farmers who are difficult to reach through traditional extension systems. Farmers can receive timely information about weather, crop management, pests, and other production risks through digital platforms, reducing their dependence on infrequent face-to-face advisory visits.

In the United States, satellite imagery, remote sensing, and machine-learning technologies are being used to improve crop monitoring and yield estimates. These tools allow large areas of farmland to be assessed quickly and help policymakers, researchers, traders, and farmers understand changing production conditions. In India, AI-based systems are increasingly being explored for weather information, pest surveillance, crop advisory services, and early identification of agricultural risks, demonstrating how digital tools can be adapted to the needs of a large smallholder farming population.

Other countries are experimenting with AI for precision irrigation, livestock monitoring, crop disease detection, market forecasting, and automated farm operations. Their experiences show that AI does not have to mean expensive machinery or fully automated farms. Sometimes, something as simple as delivering the right information to the right farmer at the right time can create significant value.

The lesson for Pakistan is clear: the country should not simply copy technologies developed elsewhere. Our farmers operate under different conditions, including water scarcity, fragmented landholdings, variable extension services, climate risks, and limited purchasing power. Pakistan should identify where better information can generate the greatest economic benefit and begin there. AI should be judged not by how sophisticated technology appears, but by whether it helps farmers reduce costs, manage risks, conserve resources, and improve their livelihoods.

Why AI Could Matter More in Pakistan’s Fields

Pakistan’s farmers face a combination of pressures that makes better information more valuable than ever. Weather is becoming increasingly unpredictable, water shortages are intensifying, pests and diseases are changing their patterns, and the costs of fuel, fertilizer, pesticides, seed, and labor continue to place pressure on farm incomes. Traditional knowledge is not obsolete; it remains an essential part of farming and reflects generations of experience with local soils, crops, and seasons. But experience works best when tomorrow resembles yesterday. Increasingly, Pakistan’s farmers are discovering that it does not.

Artificial intelligence cannot replace a farmer’s judgment, nor can it predict the future with certainty. What it can do is help farmers make better decisions about when to plant, irrigate, fertilize, protect, and harvest their crops. For a country where agriculture supports millions of livelihoods and accounts for the largest share of water use, even modest improvements in decision-making could produce significant economic and environmental benefits.

Yet the obstacles are real. AI depends first on reliable information. In Pakistan, agricultural data are often scattered among government departments, research institutions, weather agencies, markets, and other organizations. Weather observations, soil information, water records, crop histories, pest surveillance, and market prices may be incomplete, inconsistent, or difficult to access. Without good data, even the most sophisticated AI system can produce poor recommendations. Strengthening agricultural data collection, quality, sharing, and coordination must therefore be treated as an essential investment rather than a technical side issue.

Access is equally important. Most Pakistani farmers operate relatively small holdings and cannot afford expensive sensors, specialized equipment, or complex digital systems. For many of them, a simple mobile phone advisory in Urdu or a regional language could be more useful than sophisticated machinery that is difficult to purchase or operate. AI will have real value only when its benefits can reach ordinary farmers in an affordable, understandable, and convenient form.

Trust will also determine whether farmers actually use AI-based advice. A farmer may hesitate to follow a recommendation that conflicts with decades of personal experience. Building confidence will require field demonstrations, transparent information, farmer participation, and evidence that recommendations genuinely improve results. The most effective approach is therefore unlikely to be technology replacing experience, but technology works alongside farmers, extension workers, researchers, and local knowledge.

Pakistan does not need to introduce AI everywhere at once. It should begin with problems that impose the greatest costs on farmers. Water management is an obvious starting point. Instead of relying on fixed irrigation schedules, AI could help estimate when crops actually need water, potentially reducing waste while protecting yields. Pest and disease monitoring offers another opportunity, particularly where early detection can prevent small problems from becoming expensive outbreaks and reduce unnecessary pesticide applications.

More accurate weather and crop forecasts could similarly help farmers adjust planting dates, irrigation, crop protection, and harvesting decisions. But success should not be measured by how advanced an AI system appears. Its real value should be measured by whether it saves water, reduces input costs, prevents crop losses, improves productivity, or raises farm income.

Finally, technology must remain practical. Ideally, a farmer should not need to understand the algorithms working behind the system. The service should simply provide useful guidance: when to sow, when to irrigate, whether a crop is showing early signs of disease, or whether changing weather conditions require action. If AI can deliver that information reliably and affordably, it could become one of the most useful new tools in Pakistan’s agricultural toolkit.

Conclusion

Pakistan’s agricultural future will depend not on choosing between traditional farming wisdom and artificial intelligence, but on combining the strengths of both. Farmers possess generations of knowledge about their land, crops, and local conditions, while AI can process vast amounts of weather, soil, satellite, pest, water, and market information to provide earlier and more precise guidance. This combination could help farmers manage increasingly unpredictable seasons, conserve scarce water, reduce input costs, prevent crop losses, and improve farm incomes. However, AI will deliver these benefits only if Pakistan invests in reliable agricultural data, affordable digital services, local-language platforms, farmer training, and systems that earn farmers’ trust. Technology should begin where it can solve the most pressing problems rather than being adopted simply because it is new. Ultimately, the future of intelligent agriculture is not about replacing the farmer with a machine. It is about giving the farmer better information, at the right time, to make smarter decisions.

Please note that the views expressed in this article are of the author and do not necessarily reflect the views or policies of any organization.

The writer is a former Pakistan Ambassador and Permanent Representative to FAO, WFP and IFAD and can be reached at nriyaz60@gmail.com

AI in Pakistan's Agriculture: Empowering Farmers

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