Agriculture today is facing mounting pressures. Around the world, farmers and food companies are contending with climate volatility, a shrinking agricultural workforce, soil degradation, falling groundwater tables, and increasing geopolitical uncertainty. Nearly one-third of the world’s soils are already degraded, while groundwater levels are dropping in 71% of major aquifers worldwide. Global demand adds to the stress: with the population projected to reach almost 10 billion by 2050, we must collectively figure out how to produce far more food even as resources shrink.
These intertwined crises make clear that incremental improvements will no longer be enough. The world needs a fundamental shift in how food is produced, managed, and distributed. Traditional approaches, which rely on heavy input use, expansion of cultivated land, and modest yield gains, will not be able to meet the scale or urgency of the challenge at hand. What is required is a complete agricultural transformation that unlocks exponential gains.
On the positive side, recent technological breakthroughs offer a credible pathway to future-proof agriculture. This potential is captured in the World Economic Forum’s new report titled Shaping the Deep-Tech Revolution in Agriculture. The report highlights seven frontier technology domains that could derisk food production. These domains include Generative AI, Computer Vision, Edge Internet of Things, Satellite-enabled Remote Sensing, Robotics and autonomous systems, CRISPR-based gene editing, and Nanotechnology, all of which collectively represent a new paradigm of tools that can transform every stage of agriculture, from seed to soil to supply chain.
Today, these technologies not only exist in concept but are also being deployed across the globe and in India. For instance, Generative AI has powered precision agriculture and farm advisory allowing farmers to get timely advice with limited dependence on traditional extension agents. Examples of Gen AI deployment include Digital Green’s recent partnership with CISCO to provide Gen-AI-enabled advisory to over 50,000 farmers in Andhra Pradesh. Similarly, the falling costs of imaging sensors have fuelled the use of computer vision in agriculture. These computer vision applications are supporting farmers in detecting plant stress, pests, and nutrient deficiencies with high accuracy simply from images or videos. A great example of this technology is Wadhwani AI’s Cotton Ace, which enables farmers to identify the best time for spraying pesticides based on images of pheromone traps deployed on cotton farms. These advancements are not limited to digital technologies, with biotech emerging as a key leverage for climate resilience. Gene-editing tools, especially when combined with AI, have significantly reduced the time from lab-to-fields for new varieties of crops. ICAR’s development of 2 climate resilient varieties of rice exemplifies the use of advanced gene-editing techniques.
Building the Ecosystem Needed to Scale Agri Deep-Tech
While each of these technology domains holds transformative promise, they need an ecosystem of support to deliver impact at scale. This support ecosystem includes: a) forward-looking policy and regulation, b) accessible finance for R&D and adoption, c) skilled human capital capable of bridging agronomy and technology, and d) a strong digital public infrastructure that ensures high-quality, interoperable data. Without these foundational pillars working together, even the most advanced technologies will struggle to move beyond pilots and achieve adoption.
India has already made significant strides in building this comprehensive ecosystem. On the policy front, initiatives such as the Digital Agriculture Mission signal a national commitment to modernize agriculture through data-driven and technology-enabled approaches. The government has also created structured pathways for regulating drones, remote sensing, and biotechnology, enabling innovators to experiment and scale responsibly. In terms of digital foundations, India is constructing one of the world’s most ambitious agricultural data infrastructures through AgriStack, a suite of digital public goods including farmer registries, geo-tagged land records, and crop-sown databases. When combined with platforms like Bhashini, which enable AI solutions in Indian languages, such digital infrastructure could dramatically lower the barriers for start-ups, cooperatives, and state agencies to deliver targeted, multilingual advisory services, credit solutions, and precision-farming tools at scale.
India has also been proactive in catalyzing research and innovation financing. Agencies such as BIRAC, through programmes like the Biotechnology Ignition Grant, have funded hundreds of biotech and agri-tech innovations.
Taken together, these developments suggest that India has the intent to move beyond siloed innovations to a cohesive effort on scaling the use of technologies for agriculture. To further bolster this ecosystem, there are several additional interventions that could be explored. Some of these include:
- Clear regulatory pathways and regulatory sandboxes: While the private sector innovates, the Government should focus on providing the guardrails that ensure that the investments can create economic, social, and environmental returns on investments. In most cases, technology advancements are occurring faster than regulatory advancements, and this can deter deployment. There is an immense opportunity to streamline regulatory and approval processes for technologies, while providing long-term clarity to innovators. This is especially important for domains such as biotech and nanotech. Additionally, regulatory sandboxes (controlled environments where innovators can test and pilot new technologies under relaxed regulations) should be promoted across clusters within the country.
- Setting the right guardrails for the future: With the increasing use of AI in agriculture, there is a requirement to set up strong AI governance frameworks that consider the security and privacy needs of the end-consumer. This will ensure that AI deployment is responsible and does not aggravate existing inequalities. Further, given that India has a large pool of small and marginal farmers, preparing contingency plans that ensure losses accrued due to AI models are necessary to stimulate adoption.
- Showcasing successes strategically: India already has several pilots that demonstrate that technology domains can create impact for agriculture. Be it Microsoft’s pilot in Baramati that significantly improved sugarcane yields, or Boomitra’s realization of carbon credits using remote sensing. Beyond the private sector, governments have also led with significant success. For instance, Telangana’s Saagu Baagu program, conceptualized with the World Economic Forum’s AI for Agriculture Innovation initiative, almost doubled the income of chilli farmers using 4 technologies. While these pilots exist, there is limited sharing of knowledge and best practices; sharing successes methodically, rather than through one-off convenings or publications, is essential to tap into the latent demand for promising technologies.
- Unlocking capital through Public Private Philanthropic Partnerships (PPPPs): Finally, scaling deep-tech for agriculture requires different forms of capital at different phases of the technology lifecycle. While isolated funds exist for R&D followed by awards, seed funds, grants, and venture capital exist, there is a need for a single system integrator to consolidate these sources of capital and streamline funding across the entire lifecycle of an innovation. This could be visualized as a platform that classifies innovations as per their lifecycle and then graduates them with information on different forms of capital and their sources. The capital graduation could initially include public and philanthropic capital, followed by blended finance, and then purely commercial private capital.
