Deep Tech Revolution in Agriculture

• The World Economic Forum (WEF) published a report titled “Shaping the Deep-Tech Revolution in Agriculture” under its Artificial Intelligence for Agriculture Initiative (AI4AI).

About Deep Tech Revolution in Agriculture

• Agriculture is entering a new era — one driven not just by machines, but by intelligent systems that learn, adapt, and collaborate. Artificial intelligence, robotics, biotechnology, and data networks are converging to create a deep-tech ecosystem that can make farming more precise, profitable, and sustainable.

Convergence of Technologies in Agriculture

• Swarm Robotics: Uses small coordinated robots powered by AI and edge IoT to carry out tasks such as weeding and harvesting.
• Precision Farm Management: Combines data from sensors, satellites, and AI models to determine the best use of water and fertiliser.
• Agentic AI: Works independently to plan and manage crop cycles or supply chains without continuous human input.
• Carbon Reporting: Uses AI and satellite data to measure soil carbon accurately and help farmers access carbon markets.

Seven Deep-Tech Domains Driving Change

1. Generative AI: Plans sowing and predicts pest attacks so farmers can act early and avoid losses.
2. Computer Vision: Detects crop diseases and grades produce automatically to reduce spoilage.
3. Robotics and Drones: Handle sowing, spraying, & harvesting to save labour and improve accuracy.
4. Edge IoT: Uses local sensors to control irrigation and fertiliser even without stable internet access.
5. Remote Sensing: Tracks soil moisture and crop health with satellites for accurate farm planning.
6. CRISPR: Develops drought- and pest-resistant crop varieties for higher yield and durability.
7. Nanotechnology: Applies fertilisers and pesticides directly to plants, saving input and protecting soil.

Barriers to Technology Adoption

• High Costs: Advanced tools like drones and IoT systems are expensive, making them out of reach for small farmers.
• Regulatory Delays: Gene-editing methods like CRISPR face slow approval and public concerns, which restrict their wider use.
• Data Gaps: Poor internet networks and limited data sharing reduce the accuracy and reliability of AI systems in rural areas.
• Environmental Risk: Nanotechnology lacks sufficient long-term studies on its environmental impacts.
• Field Variability: Computer vision performs poorly under uneven lighting and varying crop stages.

Way Forward

• Policy and Regulations: Governments should introduce adaptive rules and regulatory sandboxes for AI, data use, and gene editing.
• Finance and Investments: Blended finance and concessional loans enable small farmers to adopt advanced agricultural tools.
• Human Capital: Skilled professionals trained in both agronomy and emerging technologies are essential for applying deep-tech solutions effectively.
• Data and Digital Infrastructure: Reliable rural connectivity and unified data standards ensure accuracy and scalability in AI-driven farm operations.
• Innovation Support: Collaboration among universities, startups, and international partners drives research, testing, and expansion of deep-tech in agriculture.

“Deep-tech innovations can transform Indian agriculture into a resilient and high-yield sector; as PM Modi said, ‘Technology should empower farmers, not replace them.’ Success depends on policy support, finance, and skill development.”

Reference: Live Mint

PMF IAS Pathfinder for Mains – Question 414

Q. Discuss how the integration of AI-driven data analytics, gene editing (CRISPR), and Internet of Things (IoT) can address the twin challenges of low productivity and climate vulnerability in Indian agriculture. (150 Words) (10 Marks)

Approach

• Introduction: Write a brief introduction about the integration of technology in agriculture.
• Body: Discuss how the integration of technology addresses the twin challenges of agriculture and suggest a way forward.
• Conclusion: Emphasis on tech-driven agriculture with a multi-pronged approach.