Shaping the Deep-Tech Revolution in Agriculture

The report identifies seven deep-tech domains—Generative AI, computer vision, edge IoT, satellite-enabled remote sensing, robotics, CRISPR, and nanotechnology—as pivotal for future-proofing global agriculture against climate change, resource degradation, and labour shortages. It argues that the convergence of these technologies can unlock breakthrough use cases, such as swarm robotics and agentic AI, but requires a coordinated multipronged approach across policy, finance, human capital, and digital infrastructure to scale.

• Global agriculture faces a convergence of five primary pressures: shifting workforce dynamics (with a global average farmer age of around 60 years), climate change and intensifying weather extremes, natural resource degradation, rising food demand, and geopolitical instability.
• Generative AI (GenAI) can provide tailored farmer advisory, simulate climate risks, and accelerate crop variety development, though its effectiveness is currently hindered by a lack of high-quality hyperlocal training data and the risk of 'hallucinations'.
• Computer vision enables real-time detection of pests, diseases, and nutrient deficiencies, as well as automated grading of produce, but faces challenges due to on-field variability in lighting and plant appearance.
• Edge IoT allows for real-time, on-farm data processing and autonomous decision-making, which is critical for rural areas with poor internet connectivity, although high capital costs and limited interoperability remain barriers.
• Satellite-enabled remote sensing provides affordable, large-scale monitoring of crop health and water stress, but its accuracy is limited in fragmented farmlands, multi-cropping systems, or areas with heavy cloud cover.
• Robotics and swarm systems can offset agricultural labour shortages by automating repetitive tasks like weeding and harvesting, though high initial costs often limit adoption to large commercial farms.
• CRISPR technology accelerates the development of climate-resilient crops by bypassing traditional breeding cycles, as demonstrated by Indian rice varieties DRR 100 and Pusa DST Rice 1, which show improved tolerance to drought and salinity.
• Nanotechnology enables the precision delivery of nutrients and pesticides through nano-inputs and nano-carriers, reducing environmental impact, though long-term health and environmental data are currently lacking.
• The convergence of multiple deep-tech domains is necessary for breakthrough use cases, such as 'Agricultural digital twins' (combining remote sensing, ML, IoT, and quantum computing) and 'Agentic AI systems' (combining GenAI, NLP, edge IoT, and cloud computing).
• Scaling agri deep-tech requires optimization across five pillars: agile policy and regulations (including regulatory sandboxes), patient capital to support long R&D cycles, interdisciplinary human capital, open data/digital infrastructure, and innovation support hubs.

APA

World Economic Forum (2025). Shaping the Deep-Tech Revolution in Agriculture. https://reports.weforum.org/docs/WEF_Shaping_the_Deep_Tech_Revolution_in_Agriculture_2025.pdf

Chicago

World Economic Forum. Shaping the Deep-Tech Revolution in Agriculture. 2025. https://reports.weforum.org/docs/WEF_Shaping_the_Deep_Tech_Revolution_in_Agriculture_2025.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Shaping the Deep-Tech Revolution in Agriculture |date=November 2025 |url=https://reports.weforum.org/docs/WEF_Shaping_the_Deep_Tech_Revolution_in_Agriculture_2025.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2025shaping, author = {{World Economic Forum}}, title = {{Shaping the Deep-Tech Revolution in Agriculture}}, institution = {World Economic Forum}, year = {2025}, month = nov, url = {https://reports.weforum.org/docs/WEF_Shaping_the_Deep_Tech_Revolution_in_Agriculture_2025.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }