WEF says AI, robotics, nanotech to transform agriculture; cites case studies from India – Crypto News

New Delhi, Nov 7 (PTI) Citing several case studies from India, the World Economic Forum on Friday said seven emerging deep technologies, including generative AI, robotics and satellite-enabled remote sensing, are poised to transform agriculture.

These technologies, which also include computer vision, edge Internet of Things (IoT), CRISPR (clustered regularly interspaced short palindromic repeats), and nanotechnology, will boost resilience and productivity, while securing rural livelihoods, the WEF said in a new report.

The report, titled ‘Shaping the Deep-Tech Revolution in Agriculture’ and developed in collaboration with stakeholders from both industry and academia, comes at a time when agriculture globally faces a convergence of crises.

Rising rural-to-urban migration, intensifying climate extremes, and accelerating degradation of natural resources, particularly soil and water, are collectively threatening productivity and endangering the livelihoods that depend on agriculture.

According to the Food and Agriculture Organization of the United Nations, the world would need to significantly produce more food, to feed a growing population by 2050.

This will have to be achieved in the light of mounting pressures with one-third of the world’s soil degraded, 71 per cent of aquifers depleted, and the average farmer reaching around 60 years old.

The WEF said these seven technology domains have the potential to trigger fundamental shifts in how crops are grown, monitored, protected, and distributed consequently improving productivity, sustainability, and resilience across the sector.

The report also highlighted the potential of converging these technologies for high-impact use cases such as autonomous swarm robotics, precision farm management, agentic AI systems, and carbon reporting.

It showcased use-cases such as climate resilient rice varieties that emit 20 per cent less emissions, precision agriculture in sugarcane that has improved yields by 40 per cent, and the use of remote sensing to predict supply chain risks and promote carbon finance to farmers.

The WEF called for greater efforts to scale deep-tech innovations that can help reimagine agricultural systems and address the pressures and urged governments to adopt agile policies and regulatory sandboxes to keep pace with technological advancement.

The report was released by the WEF’s Artificial Intelligence for Agriculture Initiative (AI4AI).

Since 2021, the AI4AI initiative has unlocked commitments to provide digital technologies to more than 895,000 farmers in India.

AI4AI has promoted multistakeholder partnerships, including among governments, the private sector, academia, start-ups and civil society to generate evidence on the transformational impact of tech in agriculture.

Building on lessons learned in India, AI4AI has supported the conceptualization of similar initiatives in the Kingdom of Saudi Arabia, Colombia and Brazil.

The WEF said irregular rainfall and increasing temperatures have already led to losses of close to 65 per cent in several horticultural crops.

It cited a case study about the Indian Council for Agricultural Research (ICAR) developing climate-resilient rice in the country.

Conventional crop-improvement methods have long breeding cycles and limited precision, slowing the development of varieties suited to dealing with emerging climate and disease pressures.

To overcome this, ICAR researchers used CRISPR-based genome editing to develop two rice varieties. The first, DRR 100, has improved tolerance to drought, salinity and climate stresses. It can lead to a 19 per cent increase in yield and a 20 per cent decrease in greenhouse gas emissions.

The other variety, Pusa DST Rice 1, can increase yields by 9.66 per cent to 30.4 per cent in saline and alkaline soils, and could potentially lead to a 20 per cent increase in production.

Another case study referred to remote sensing for efficient crop insurance under India’s Pradhan Mantri Fasal Bima Yojana (PMFBY).

Crop-loss assessments for insurance conventionally rely on manual crop-cutting experiments (CCEs), but this method is slow, often inaccurate and can lack transparency, delay claim settlements and cause disputes.

To solve this, PMFBY implemented a technology-driven solution centred on remote sensing. The system uses satellite imagery to monitor crop health, while high-resolution drones and a dedicated mobile app for geotagged, real-time data collection supplement this.

This multilayered approach replaces subjective manual assessments with objective, verifiable data, streamlining the entire loss-estimation process.

WEF said such tech transformation can ensure faster and more accurate claim settlements, providing crucial financial support to farmers when they need it the most.

The use of objective satellite data enhances transparency and builds trust, while the comprehensive data on crop health also empowers the government and insurers to better manage agricultural risks and develop more effective insurance products.

Other case studies from India talked about local programmes for developing nano inputs to bolster resilience, use of satellite-enabled remote sensing for Carbon monitoring, reporting and verification (MRV), and using digital public infrastructure to scale generative AI and natural language processing.

Bhashini, launched under India’s National Language Translation Mission, is a Government of India-led initiative to develop a public digital infrastructure for language translation and speech technologies in Indian languages.

It provides open-source datasets, language models and APIs for automatic speech recognition, machine translation and text-to-speech systems.

For deep-tech start-ups, Bhashini reduces the barriers to entry by enabling the integration of vernacular voice and text interfaces into their use cases. This facilitates broader outreach to non-English-speaking users, supports regional deployment at scale and lowers the cost of building multilingual digital tools.