India’s use of artificial intelligence in agriculture is entering an important phase of maturity. In 2026, the focus is expected to move beyond pilots and isolated experimentation toward questions of integration, reliability, and scale.
This shift is less about dramatic technological breakthroughs and more about how digital tools are absorbed into the everyday functioning of agricultural institutions and value chains.
Over the past decade, a wide range of AI-driven applications have been developed for agriculture, from crop advisory and pest detection to yield estimation.
While these initiatives have contributed to experimentation and learning, their impact has often been uneven. Experience suggests that technology alone does not guarantee adoption or outcomes. What increasingly matters is how intelligence is embedded within existing systems that shape agricultural decisions, including government programmes, financial institutions, cooperatives, and supply chains.
One factor shaping this transition is the gradual strengthening of digital foundations across the agricultural ecosystem. Farmer registries, crop surveys, decision-support systems, and data-sharing mechanisms are improving the consistency and availability of information.
As these elements evolve, they create conditions in which AI can support institutional decision-making rather than operate only at the level of individual applications. In upcoming year, solutions that align with such shared frameworks are likely to find wider acceptance than those functioning in isolation.
Expanding institutional capacity
Institutional capacity for applying artificial intelligence in agriculture is also expanding within academia and the public research system. For example, Indian Institute of Technology Ropar hosts a Centre of Excellence in Agriculture. Such initiatives reflect a distributed approach to capability building, where interdisciplinary research and digital tools are being explored to address challenges related to productivity, sustainability, and risk, rather than a single centralised model.
This broader context also clarifies where AI is most likely to demonstrate value. Farmer-facing advisory remains relevant, but it has often struggled to scale sustainably, particularly where recommendations are difficult to validate or where incentives are weak.
In contrast, momentum is building in adjacent areas where outcomes can be more directly measured. These include yield forecasting for procurement planning, risk assessment for rural credit, assisted processing of insurance claims, quality grading in food processing, and demand forecasting across perishable supply chains. In such cases, even modest improvements in accuracy or timeliness can have meaningful economic and operational effects.
From a technical standpoint, agricultural AI is moving toward multimodal approaches that combine multiple sources of information. Decisions in agriculture are influenced by weather patterns, soil conditions, crop varieties, and local practices, making single-source models insufficient. Integrating satellite imagery, climatic data, field observations, and historical records enables more context-aware analysis.
Within this architecture, generative and agent-like AI systems are increasingly being explored not only as interfaces, but also as coordinators of routine workflows, such as initiating service requests, lodging preliminary insurance claims, or routing cases within institutional systems. These capabilities are typically deployed with defined boundaries and human oversight, reflecting the sensitivity of agricultural decisions.
Limits of current approaches
At the same time, the limits of current approaches are becoming clearer. Data quality and representativeness remain uneven across regions and farm sizes, raising questions about how well models developed in one context perform in another. There are also concerns around transparency and accountability when algorithmic assessments influence credit, insurance, or advisory decisions, particularly for farmers with limited digital footprints. Alongside infrastructure and cost constraints at the last mile, these factors suggest that the impact of agricultural AI will depend as much on governance, inclusion, and operational design as on advances in technology.
Structural pressures on land use further frame this discussion. With limited scope for expanding cultivated area due to urbanisation, infrastructure development, and fragmentation of holdings, future gains in agricultural output are increasingly expected to come from improvements in productivity and resource efficiency. Digital decision-support tools, including AI-based systems, are being examined in this context as one means of supporting more efficient use of land, water, and inputs, rather than as substitutes for broader agrarian policy measures.
As deployment expands, questions of trust and accountability become increasingly important. When AI outputs influence agricultural decisions, errors can carry real consequences for livelihoods and public resources. There is therefore growing recognition of risk-based approaches to AI governance, where expectations around transparency, oversight, and accountability vary according to the potential impact of the application. Higher-stakes uses, such as credit or insurance assessment, are receiving greater scrutiny than lower-risk decision-support tools. Human oversight, auditability, and clear responsibility remain central to responsible deployment.
Last-mile realities
Equally significant are last-mile realities. India’s agricultural landscape is diverse, with variations in language, connectivity, and institutional capacity. Solutions that assume continuous internet access or rely heavily on text-based interaction often face barriers. Designs that prioritise offline functionality, voice-based interfaces, and assisted workflows for field personnel are more likely to align with conditions on the ground. In many cases, usability and contextual fit matter as much as technical sophistication.
India’s AI outlook for agriculture in 2026 is, therefore, best understood as evolutionary rather than transformative. Progress is shaped by incremental integration, institutional learning, and operational constraints alongside advances in technology. The extent to which AI contributes to agricultural resilience will depend not only on innovation, but on how carefully and responsibly these tools are embedded within the systems that govern production, finance, and distribution.
The author is Fujitsu Fellow & Global Fujitsu Distinguished Engineer.
Published on January 3, 2026
