AI in Indian Agriculture: Bridging Promise and Rural Reality
AI can transform Indian Agriculture, but challenges like cost, access, trust, and data rights may hinder its impact on rural India.
Introduction: Promise of AI for a Better Bharat
In his article “Building AI for Bharat” (The Financial Express, July 16, 2025), Rameesh Kailasam presents a hopeful vision of how artificial intelligence (AI) can change India’s future. He focuses on agriculture, where almost half the population works, and suggests that AI could solve many of the problems farmers face today. He supports this idea with examples of government efforts, such as the IndiaAI Mission, and private companies like Krutrim and ITC MAARS.
But while the article paints an exciting picture, it also leaves out many important issues. This essay argues that the article is too optimistic and does not give enough attention to the real challenges of using AI in rural India. These challenges include economic problems, lack of trust, poor internet access, data privacy, and the danger of excluding the very people AI claims to help.
AI in Agriculture as a National Opportunity
Kailasam’s main idea is clear: if artificial intelligence (AI) is used well in Indian farming, it can raise farmer incomes, improve food security, and support national self-reliance (Atmanirbhar Bharat). He praises examples like ITC MAARS, a platform that uses both physical help and digital tools (“phygital”) to give farmers advice, help with crop planning, and connect them with markets.
This idea sounds good on paper. The government is indeed pushing forward with AI initiatives. Startups are building large language models (LLMs) that speak Indian languages. Some platforms have already helped millions of farmers. The author says these are signs that India is on the right path.
But this argument leaves out several important facts.
Problem of Affordability
Many small and marginal farmers in India earn less than ₹10,000 per month. Yet, many AI-based services are expensive. For example, using apps for crop diagnosis, weather updates, or digital marketplaces often requires a smartphone, a data plan, and sometimes monthly fees.
Kailasam does not talk about the cost of these services. Nor does he explain how farmers will afford AI-based tools when most of them struggle to buy seeds and fertiliser. Without government subsidies or community-based models, AI may only be available to richer or large-scale farmers.
So, the idea that AI can lift all farmers equally is unrealistic. It might even deepen the divide between rich and poor farmers.
Challenge of Fragmented Landholdings
India’s farms are very small and broken into tiny plots. The average landholding is just about 0.6 hectares. Using AI tools like satellite imaging, drone mapping, or sensor-based irrigation on such small plots may not be cost-effective.
Moreover, when one farmer uses modern tools and others do not, the benefits are limited. The article does not mention this challenge of land fragmentation and how it affects AI’s usefulness.
For AI to truly help, there needs to be a way to bring smallholders together—perhaps through cooperatives or Farmer Producer Organisations (FPOs). But even then, the path is not simple.
The Digital Divide and Trust Issues
Another serious problem is that many farmers still do not trust digital tools. They prefer traditional knowledge, advice from fellow farmers, or local vendors. Even those who want to use AI tools may not know how to operate them. This lack of digital literacy is especially high among older farmers and women.
In many villages, internet connectivity is poor. Electricity is not always reliable. Smartphones are shared within families, and not every user is trained. AI platforms often depend on real-time data and cloud services. If farmers cannot use them smoothly, they may give up.
Kailasam’s article does not explore this digital divide. He does mention the need for personalised, language-specific platforms—but he ignores the deeper problem of access and trust.
Data Privacy and Ownership Concerns
AI needs data to work. This includes soil reports, crop photos, weather updates, farm sizes, income levels, and even personal details of farmers. But who owns this data? How is it protected? Are farmers paid for sharing it?
These are serious questions. There have been reports in other countries where farmers shared data with tech companies, and later lost control over it. In India, too, data privacy laws are still developing.
Kailasam mentions Krutrim and Sarvam AI building large models using “contextual data”. But where does this data come from? Did farmers give consent? Are they compensated?
Without clear rules, AI in farming may become just another way to take advantage of the poor.
Environmental and Social Risks
Technology can solve problems—but it can also create new ones. AI tools may suggest high-yield crops, fast-growing seeds, or chemical-heavy methods to increase production. This can lead to soil damage, overuse of water, or reduced biodiversity.
Moreover, as AI automates tasks like crop planning or pest control, there is a risk that rural jobs—especially for farm labourers—will disappear. The article does not discuss these possible side effects. In a country like India, where farming provides not just food but also work, this matters deeply.
Risk of Creating a Two-Speed Agriculture
By promoting AI without solving the access problem, India may end up with two kinds of agriculture:
- A modern, AI-powered system used by large farmers, agribusinesses, and export producers.
- A traditional, low-tech system followed by small and marginal farmers, left behind in the race.
This could lead to deeper rural inequality. The very goal of AI—to help the weakest—would be lost.
Kailasam praises ITC’s MAARS platform, but this is a private model that may not scale to all regions. If similar platforms are run by commercial firms, they may focus on profit rather than equity.
Some Points Well Made
To be fair, the article does highlight some real positives. It shows that India is not waiting to copy the West. Instead, it is building its own AI tools in Indian languages. That is a big step.
The author rightly notes that India has bright minds and a strong startup culture. If AI is developed in the right way—one that matches local needs and languages—it can help.
He also reminds us that AI is not just for farming. It can help in healthcare, education, public safety, and more. But again, these claims are general. The article lacks clear evidence or strong data to show how these gains are actually happening on the ground.
What Should Be Done Instead?
Rather than seeing AI as a silver bullet, we need a slow, careful approach. Here are some better ideas:
- Public-Private Partnerships: Let the government work with local companies and cooperatives to create low-cost AI tools for small farmers.
- Build Digital Literacy First: Train farmers, especially women and youth, to use phones, apps, and digital tools before introducing complex AI systems.
- FPO-based AI Adoption: Use Farmer Producer Organisations to bring together smallholders and help them share costs, knowledge, and benefits of AI.
- Data Rights Framework: Make sure farmers know who uses their data, and that they are paid or protected when they share it.
- Language and Culture Fit: AI tools must match the local language, weather, crop patterns, and even cultural beliefs of farmers.
- Invest in Research on Impact: Before scaling up, pilot projects must be tested well—with third-party audits to measure results honestly.
Conclusion: A Vision Needing Balance
Rameesh Kailasam’s article is full of hope, ambition, and pride in India’s AI journey. He rightly believes that India has the talent and drive to build its own future. He is also correct in choosing agriculture as the most important sector for change.
But his argument is too one-sided. It praises success stories and government missions but ignores economic, social, and ethical challenges. It assumes that all farmers will accept AI, when in fact most are not yet ready. It talks about big data but says little about privacy. It celebrates technology without checking who benefits most.
A balanced view is needed. AI must not replace people but empower them. It must not increase inequality but reduce it. It must not speak only in code—but in every Indian language, with humility and fairness.
Only then will AI truly serve Bharat—not just as a tool of pride, but as an engine of justice.
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The Source’s Authority and Ownership of the Article is Claimed By THE STUDY IAS BY MANIKANT SINGH
