National Technology Day is an occasion to reflect not merely on what technology has enabled, but on what it must enable next. For an occupation like agriculture that employs about half of the nation’s workforce and is essential to the nation’s food and nutritional security- the farm stands as the key frontier for technology in the sector. As one of the primary enablers of the next phase of Indian agriculture, Artificial Intelligence (AI) is playing a key role in developing agriculture by its next phase of growth through innovation and resilience based on precision, efficiency and sustainable productivity.
India’s agricultural growth story has been remarkable. Decades of policy effort, farmer enterprise, and scientific advancement have made the country one of the world’s largest producers of food, milk, oilseeds, and horticulture. Yet a central challenge continues to grapple India’s agronomy; the productivity remains significantly lower than global standards. Most of the agricultural households in India consist of small or marginal farmers, who operate under thin margins, unpredictable weather and poor access to expert advice. This environment of high-risk farming presents the greatest opportunity for agricultural productivity improvements through the application of AI.
The most immediate opportunity for AI lies in risk reduction, not yield maximisation. Preventing a loss can sometimes be more important for a marginal farmer than creating a marginal increase in profit. AI-based early warning systems, along with satellite imagery and predictive analytics, are already proving they have the potential to enable farmers to take preventative action before erratic rainfall, pests and soil degradation cause irreversible loss.
By processing large volumes of climatic, environmental, and agronomic data at speed and scale, AI can equip farmers with the kind of decision support that was previously accessible only to large commercial operations with dedicated agronomy teams.
India is better positioned than most to operationalise these capabilities, because the underlying data infrastructure is being built at scale. The government’s AgriStack initiative is creating a federated digital backbone through the Farmers’ Registry, geo-referenced village maps, and plot-level crop data. Over 6.4 crore farmer IDs have already been created, and the Digital Crop Survey now covers more than 25 crore plots across 17 states. The National Pest Surveillance System (launched in 2024) leverages AI/ML and image recognition to issues 10,154+ real-time, localised pest-management advisories.
In sum, AI forecasts and detection systems move agriculture from reactive to predictive: drought, flood or disease no longer catch farmers entirely off-guard. These risk-buffering tools enhance resilience, stabilize yields, and preserve smallholder incomes in a climate-volatile era.
Crop health monitoring using satellite and drone imagery, nutrient and input optimisation through soil datasets, pest and disease forecasting based on historical and climatic patterns, and risk advisories anchored in farm registries represent areas where AI adoption can scale fastest. These are domains where the data foundations are strongest and the return on investment is clearest. For input-intensive operations such as animal nutrition and crop protection, AI-driven advisory systems can meaningfully reduce wastage, lower the cost of production, and improve farm profitability simultaneously.
The fisheries sector offers another illustration of AI’s near-term readiness. India is the world’s second-largest fish producer. Pond-based aquaculture, with its relatively controlled environment, is particularly amenable to sensor integration, real-time water quality monitoring, and predictive feed management. The return on AI investment in this segment is measurable and the barriers to deployment are lower than in open-field farming. As India’s agri-value chain diversifies, sectors such as this will serve as proving grounds for broader adoption.
At the same time, intellectual honesty demands acknowledgement of where the gaps remain. India currently spends approximately 0.3 to 0.4 percent of its agricultural GDP on research and development, compared to roughly 0.7% in the US. The diversity of agro-climatic zones, the fragmentation of landholdings, and the heterogeneity of farming practices make it far harder to build and deploy AI models that are universally applicable. Connectivity and digital literacy constraints persist in many geographies. And there is a real risk that, without adequate oversight and farmer-centric design, AI solutions become technology-led disruptions that bypass the very communities they are meant to serve.
To prevent this, we need to rethink how we view AI for agriculture with an emphasis on risk-adjusted profits instead of merely increasing output. For example, An AI system that helps a farmer protect income in a drought year delivers more durable value than one that promises yield increases under ideal conditions. This requires ongoing investment in R&D for hyper-local datasets and climate analysis, greater cross-sector cooperation with agri-businesses, technology providers, researchers, and government organisations, and the establishment of digital infrastructure that allows for last-mile connectivity to all communities.
Global evidence shows that agricultural technologies that utilize AI and precision agriculture have provided an increase of between 10-20% in agricultural yield for developed countries. Since India has much lower agricultural productivity than other developed countries, even modest increases can yield significant income improvements for smallholders and marginal farmers. The opportunity is real, but it must be pursued with the same rigour that was applied to the original Green Revolution: with science-led discipline, institutional commitment, and an unwavering focus on farmer welfare.
National Technology Day is a reminder that technological progress is not an end in itself, it is a means to a larger national purpose. In agriculture, that purpose is unambiguous: more resilient livelihoods, greater food security, reduced import dependence, and a farming community equipped to navigate an increasingly climate-volatile world. Artificial intelligence, applied with intent and designed around the farmer has the potential to be the defining technological lever of the next Green Revolution. The data foundations are being laid. The policy architecture is taking shape. What is needed now is the collective will to ensure that this transformation reaches every farm, in every corner of the country.
(The views expressed are personal)
This article is authored by Ashima Seth, chief digital and information officer, Godrej Agrovet Limited.
