Language is the primary barrier to significant adoption of AI at scale.

Analytics India Magazine
By: Smruthi Nadig, Technology Journalist

Farmers have long relied on instinct, inherited wisdom, and hurried conversations with local input dealers to decide when to sow and when to sell. Some seasons reward them, and others leave them staring at wilting crops or falling prices after harvest.

Today, the decision-making is starting to shift. Before heading to the field, farmers can open a mobile application, enter crop details, check weather advisories, and receive tailored recommendations on sowing, irrigation and inputs. 

This guidance is no longer coming from neighbours or traders. It is increasingly generated by AI-powered advisory systems that integrate satellite imagery, soil data, climate history, and market signals. Across India, this is moving from isolated pilots to a broader preview of agriculture’s near future.

An AI-First Push from the Budget

The Union Budget 2026-27 has placed digital agriculture and artificial intelligence squarely at the centre of what policymakers describe as an “AI-first Viksit Agriculture” vision. The thrust is to embed AI into agricultural infrastructure to drive productivity, climate resilience, and income stability.

Industry leaders see this as a structural shift.

“The Budget acknowledges that agritech isn’t a niche. It is central to India’s strategic priorities of improved farmer incomes and food security,” says Anand Mahurkar, Founder and CEO of Findability Sciences. 

Mahurkar states that with AI and machine learning embedded in digital agriculture infrastructure, it is possible to anticipate pest outbreaks before they devastate crops, tailor irrigation and input use down to the field level, and deliver climate-smart advisories that reduce risk. He says AI transforms agriculture from a reactive struggle to proactive, data-driven growth. 

The focus on integrating AI into initiatives such as AgriStack, India’s digital agriculture backbone, signals a move beyond pilots toward systemic adoption. At the same time, the budget emphasises strengthening value chains through farmer-producer organisations, women-led enterprises, and better market linkages.

Gaurav Bagrodia, President, Business at SaralSCF, BlackSoil, says these measures are key to improving income stability and driving formalisation. He notes that structured credit and financing can unlock more predictable cash flows across agri-value chains. However, not everyone believes technology alone can solve agriculture’s deeper challenges. 

“Technology is just an enabler,” says Dharshan Puttannaiah, farmer and Member of the Karnataka Legislative Assembly from Melukote. He argues that while AI tools are useful, they risk overshadowing deeper economic issues that farmers face.

Closing the Information Gap

For Vilas Dhar, President of the Patrick J. McGovern Foundation, the core issue AI addresses is information asymmetry. He says farmers understand their land deeply, but they lack access to commercial-grade intelligent weather forecasting, market pricing, and supply chain visibility. 

AI tools are helping to bridge this gap. The McGovern Foundation works with organisations such as Digital Green, which provides AI-powered advisory services to smallholder farmers. 

FarmerChat, a multilingual AI chatbot, provides hyperlocal farm advice via voice, text, images, and video on WhatsApp and Telegram, catering to low-literacy and low-connectivity areas. 

Dhar says over five million queries have been answered across one such portfolio, with over 830,000 users across India and other developing markets. 

However, access to information does not automatically improve outcomes. 

“The way farmers are getting paid for their produce is not right,” he says, adding that without fixing pricing and market linkages, advisory systems alone cannot transform income. 

For Dhar, the metric goes beyond productivity.  “It’s not just about yield. It’s about dignity and agency, ensuring people have access to the same information,” he says. 

Measuring What Matters

AI in agriculture often comes with a lot of buzzwords, but its impact is increasingly being measured. Dhar says evaluations focus on multiple layers: cultivation costs, yield, revenue realisation, and adoption.

“What we’ve seen is up to a 10x improvement in the cost of agricultural practice in certain deployments,” he says, referring to reductions in input and operational costs.

Revenue, he adds, is influenced not just by yield, but by timing- knowing when to sell can significantly change income.

For companies operating in the commercial agritech ecosystem, similar accountability is emerging.

Amit S, Chief Digital Officer at UPL Ltd’s Nurture.retail, states that outcomes are validated through control and treatment field studies and farmer feedback, not internal engagement metrics. 

In large-scale pilots, he says, digitally enabled agronomy and input-optimisation programmes have reduced input usage while yields.

This emphasis validation reflects a broader concern that AI recommendations could become generic.

Guardrails Against “Generic Advice”

India’s agricultural landscape is deeply fragmented with small landholdings in diverse climate zones. A district-level recommendation can be irrelevant, even damaging, for a specific plot.

To prevent this, Nurture.retail says it uses what it calls “100+ Parameter Precision,” integrating soil data, microclimate conditions, and historical patterns to generate plot-level recommendations.

It also uses Retrieval-Augmented Generation (RAG), grounding its generative AI in a validated “knowledge vault” that includes inputs from ICAR. If the system lacks a reliable basis for a recommendation, it defaults to expertise.

For smallholders like Ramesh, such guardrails are not technical footnotes; they are financial lifelines.

Language, Literacy, and Adoption

Even the most advanced systems fail if the farmer cannot understand or trust them. “Language is the primary barrier to significant adoption of AI at scale,” Dhar says.

Multilingual interfaces and voice-based systems are becoming essential. Platforms are increasingly designed to operate in regional languages and dialects, ensuring farmers understand not just what to do, but why.

“Adoption of tech is also very hard,” Puttannaiah says, pointing to low awareness and fragmented landholdings. In many cases, he argues, technology may still be secondary to more fundamental issues.

Dhar echoes this concern. “You cannot simply drop a tool into a community and expect change. You have to co-design tools with the community, he says. 

Field-level support—extension workers, agronomists, and feedback loops—often determines whether AI systems are used or ignored.

Data Ownership and Trust

As AI platforms gather granular data, soil health, cropping patterns, and GPS coordinates, questions of ownership and benefit become more pronounced.

“We never want to be in a world where we live the traditional model of giving up all of your data, and maybe you get a benefit,” Dhar says. For commercial players, maintaining trust is equally important. If a lower-cost intervention is more effective, the system is designed to recommend it. 

Field teams are also increasingly evaluated on farmer success and yield improvements rather than sales volumes.

Despite improvements in efficiency, the question remains whether these gains translate into stable incomes.

He adds that AI integration with AgriStack also supports women farmers by improving decision-making, increasing access to finance and trade, and reshaping household economics.

According to foundation data, Digital Green’s programmes have led to an average 24% increase in income for 5.2 million farmers, 70% of whom are women.

AI could shift from reactive to predictive, allowing systems to anticipate pest outbreaks, droughts, or price crashes. 

Dhar notes resilience is assessed through macro events and their long-term impacts. AI-driven risk prediction may improve revenue stability, loan access, and financial safety, but he cautions it’s too early to confirm enterprise-level benefits, describing current efforts as promising pilots.

Future of AI in Agriculture? 

India’s AI-first agriculture vision signals political will. Industry players are aligning technology stacks, and foundations are increasingly piloting models across continents. Farmers are beginning to experiment with chatbots alongside traditional practices.

The gains achieved thus far are incremental yet tangible, such as reduced input costs, slightly improved price realisation, and fewer sleepless nights spent monitoring the sky.

The larger promise of income stability, climate resilience, and dignified agency will depend on execution. Data quality, governance, infrastructure, and trust will determine whether these systems scale effectively.

Puttannaiah believes the sector needs a fundamental rethink. “We have to overhaul the entire value chain so farmers have better economic value for their effort,” he says. 

As AI becomes embedded in India’s agricultural ecosystem, its success will not be measured by the sophistication of its models, but by whether farmers see tangible improvements in their livelihoods.