Global agriculture, at present, stands at a crucial turning point. Climate change, degrading resources, demographic shifts, and geopolitical tensions are coming together to create new pressures on the world’s capacity to feed a rising population. Traditional approaches, although still important, are no longer enough to deal with the scale or speed of these issues.
The World Economic Forum (WEF) published its report titled Shaping the Deep-Tech Revolution in Agriculture, on November 6, 2025, prepared under the Artificial Intelligence for Agriculture Innovation (AI4AI) initiative. It discusses how advanced technologies could open up new pathways for transforming the agricultural sector. It identifies the deep-tech areas with the potential to reshape agriculture; highlights how these technologies intersect and influence each other; and explains their practical relevance for farming. The report further looks at how coordinated efforts among governments, industry, and research institutions could enable agricultural deep-tech to strengthen and secure the future of global food systems.
About AI4AI
AI4AI was launched by the WEF in 2021 as a global multi-stakeholder initiative to accelerate responsible adoption of AI and frontier technologies in agriculture. It aims to enable 10 million farmers—at least 30 per cent women—by 2030 through scalable digital and deep-tech solutions.
Flagship deployments as the ‘Saagu Baagu’ programme in Telangana, India, demonstrate measurable improvements in crop yields, input efficiency and market access, showcasing the potential of deep-tech when aligned with public systems.
Since 2021, the AI4AI initiative has reportedly unlocked commitments to provide digital technologies to more than 8,95,000 farmers in India.
Acknowledging the Looming Crisis in Global Agriculture
Global agriculture is moving towards a period where the pressures on food production are expected to intensify while the resources needed to sustain it would shrink. The sector is already seeing a steady decline in agricultural labour due to changing workforce patterns, rising migration to cities, and ageing rural commitments. The average age of farmers worldwide is about 60 years, a trend that is evident across major agricultural regions, such as the US and Europe. In 1960, more than 65 per cent of the global population lived in rural areas; by 2023, this had dropped to 43 per cent and projections suggest it could fall to 32 per cent by 2050, posing long-term risks to productive capacity.
These demographic pressures interact with climate challenges that are expected to significantly influence agricultural output. Even with adaptation strategies, global calorie yields from staple crops may be 24 per cent lower by 2100 if emissions continue unchecked. India is already experiencing the consequences of irregular rainfall and rising temperatures, with losses reaching nearly 65 per cent in several horticultural crops. The WEF notes that climate variability is increasing both the frequency and intensity of production shocks across regions, creating cascading risks for food systems.
The deterioration of natural resources adds another strain. Agriculture accounts for 70 per cent of freshwater withdrawals, yet 71 per cent of groundwater aquifers are already overdrawn. Soil degradation, which currently affects one-third of global soils, could extend to nearly 90 per cent of the Earth’s topsoil by 2050, threatening long-term productivity.
At the same time, the demand for food is growing. By 2050, food production would need to rise by about 70 per cent compared to 2005–07 levels and output in developing countries may need to nearly double. Yet inefficiencies persist, as around 13 per cent of food is lost after harvest and before retail, while 19 per cent is wasted at retail, in food services and in households.
Geopolitical tensions further heighten these pressures. Disruptions such as those triggered by the Russia-Ukraine war led to fertiliser prices reaching historic highs in 2022. With nearly 36 per cent of countries depending on food imports, global instability increases vulnerability to supply disruptions.
Future-Proofing Agriculture: The Role of Deep-Tech
Meeting this combination of pressures requires a fundamental shift in the ways food is produced, managed, and distributed. Deep-tech and science-driven technologies, such as artificial intelligence, robotics, edge computing, remote sensing, and synthetic biology, offer pathways to address systemic agricultural and environmental challenges. These technologies could foster inclusivity, sustainability and efficiency, helping farmers reduce cultivation costs, improve yields, secure better returns, and enhance resilience. Agribusinesses, too, rely on agritech to manage sourcing, streamline interactions with farmers, comply with evolving global standards and move towards carbon neutrality.
Despite their potential, significant barriers remain, including low adoption, limited contextual data, high upfront costs, and increasing fragmentation of data. The report builds on the work of AI4AI by presenting promising deep-tech domains, key convergences and the systems required to seed, de-risk, and scale agri deep-tech.
Seven Promising Agri Deep-Tech Domains
The report outlines seven deep-tech domains that hold transformative potential for agriculture: (i) generative AI, (ii) computer vision, (iii) edge internet of things (IoT), (iv) satellite-enabled remote sensing, (v) robotics, (vi) clustered regularly interspaced short palindromic repeats (CRISPR), and (vii) nanotechnology. Each domain offers unique applications and many of the most promising innovations emerge when these technologies converge.
Generative AI Generative AI (GenAI) could support a range of functions, from customised farmer advisory and pest management to climate-risk simulations and agentic AI systems. It could create hyperlocal practice recommendations, communicate with farmers through natural language processing and contribute to macro crop planning and new variety development. While advances in large language models and improved data availability support adoption, a shortage of high-quality data for hyperlocal models continues to limit progress.
Computer Vision Computer vision helps identify pests and diseases quickly, detect plant stress and monitor crop through image-based systems. It underpins autonomous robots and systems for grading and sorting. Even with falling camera costs and improvements in deep learning, on-field variability still restricts its widespread use.
Edge Internet of Things (IoT) Edge IoT processes data directly on or near devices, reducing latency and enabling real-time autonomous decisions, an essential function in rural areas with limited connectivity. Its applications include automated irrigation, early disease detection, and optimised fertiliser use. Key hurdles include high capital costs and limited interoperability.
Satellite-Enabled Remote Sensing Satellite-enabled remote sensing provides continuous and large-scale monitoring of farm conditions at relatively low cost. It enables crop health monitoring, stress detection, and forecasting through spatial and spectral data. When integrated with machine learning, it could offer predictive and prescriptive insights and support monitoring, reporting, and verification systems. However, its accuracy is constrained in fragmented smallholder landscapes.
Robotics (including Drones) Robotics could automate labour-intensive tasks, such as precision planting, weeding, and harvesting. Growing integration of AI perception and cloud-edge systems is improving their utility, though high capital investments hinder adoption of robotics in regions with abundant labour. The report also points emerging use cases such as autonomous or swarm robotics, where multiple lightweight robots operate collaboratively to reduce dependency on manual labour.
CRISPR CRISPR enables the rapid development of crop varieties with traits, such as drought tolerance, pest resistance, and shorter growth cycles. It accelerates breeding, allowing for yield improvements, reduced pesticide use, and better climate resilience. Regulatory and public perception challenges continue to impede wider deployment of this technology.
Nanotechnology Nanotechnology offers precision in delivering nutrients and pesticides, thereby reducing input use and environmental impact. Its applications include nano-inputs, carriers for controlled release and biosensors. Limited understanding of long-term impacts on health and environment remains a key obstacle to scale.
Tech Convergences and Breakthrough Use Cases of Agriculture
The true potential of these technologies emerges when they are combined. Convergence-based use cases illustrate how generative AI, robotics, edge IoT, satellite sensing, CRISPR, and nanotechnology could jointly address specific agricultural challenges. The report underscores that convergence is ‘central to the deep-tech revolution,’ with multi-technology use cases delivering disproportionately higher value than isolated applications.
In response to declining agricultural labour, convergence-driven solutions would need to supplement manual work with automation to strengthen operational efficiency and mitigate workforce shortages. To manage intensifying climate impacts, use cases would have to support predictive adaptation, localised risk reduction, and climate resilient decision-making. For natural resource degradation, technologies must continuously assess natural resource health, enable regenerative approaches and support data-driven decision-making to ensure sustainable resource use. Rising food demand and production demand mismatches require systems that enhance productivity while supporting more accurate crop planning. Geopolitical risks call for localised production, supply chain risks assessments, and better traceability.
Indian Case Studies Highlighting Early Transformation
The report includes case studies and points to examples that present early signs of deep-tech integration in India.
The Indian Council for Agricultural Research has developed climate-resilient rice varieties through CRISPR-based genome editing. The DRR100 variety shows improved tolerance to drought, salinity, and climate stresses, with a 19 per cent increase in yield and 20 per cent lower emissions. The Pusa DST Rice 1 strain could raise yields by up to 3.04 per cent in saline and alkaline soils, potentially increasing overall production by 20 per cent.
Another example is the use of remote sensing in the Pradhan Mantri Fasal Bima Yojana crop insurance scheme. Satellite imagery, drones, and geotagged mobile data enable crop health monitoring and automated loss assessment. This system reduces reliance on manual crop-cutting experiments and provides objective, verifiable data that supports faster claim settlements and builds transparency and trust between farmers and insurers.
The report also highlights the Bhashini platform (under the National Language Translation Mission), which allows deep-tech start-ups to create vernacular AI applications. With open-source datasets, language models and application programming interfaces (APIs), Bhashini/platform lowers development costs and extends digital solutions to non-English-speaking populations across rural India facilitating wider agritech adoption.
Optimising Agri Deep-Tech Ecosystems
Creating an environment where agri deep-tech could thrive requires strong support across five ecosystem pillars: policy and regulation, finance and investments, human capital, data and digital infrastructure, and innovation support systems. These pillars help generate ideas, reduce risks in early-stage pilots, and enable commercialisation. Strengthening them is essential for scaling innovation and achieving meaningful impact.
Moving forward
Building resilient agri deep-tech ecosystems depends on coordinated action among governments, industry, research bodies, and civil society. Scaling these technologies remains challenging, especially for smallholder farmers and agri small and medium-sized enterprises (SMEs) in emerging economies. To close the gaps in awareness, access, and adoption, collective and imperative effort is required. Aligning responsibilities across stakeholders and reinforcing ecosystem foundations could enable deep-tech to strengthen the resilience, sustainability and productivity of global agriculture at a time of mounting pressures.
To conclude, deep technologies such AI, IoT, CRISPR, robotics, and nanotechnology are the building blocks of the future agricultural revolution. These innovations can do wonders if combined with inclusive policy frameworks, digital infrastructure, and farmer empowerment. The adoption of deep-tech will make agriculture not only smarter but also climate resilient and sustainable.
© Spectrum Books Pvt Ltd.
