Deep Tech in Agriculture

The World Economic Forum’s (WEF) AI for Agriculture Initiative (AI4AI) has released a new report outlining how deep tech in agriculture can address the growing global challenges of food security, climate change, and resource degradation. Read here to learn more.

The WEF AI for Agriculture Initiative (AI4AI) has released a new report outlining how deep technologies, such as Artificial Intelligence (AI), computer vision, robotics, and satellite sensing, can revolutionize agriculture.

In the context of agriculture, these technologies have the potential to make food production more efficient, resilient, and sustainable amid mounting challenges such as climate change, resource depletion, and declining farm labour.

What is Deep Tech in Agriculture?

Deep tech in agriculture refers to the integration of advanced science and technology into the entire agricultural value chain, from soil health and crop management to post-harvest processing and market access.

It goes beyond traditional “agritech” apps and focuses on AI-driven decision systems, autonomous machinery, and data-driven precision farming that combine multiple technologies to deliver transformative outcomes.

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AI for Agriculture Initiative (AI4AI)

• Launched by: World Economic Forum (WEF).
• Objective: To bring together public and private sector stakeholders to scale agritech innovations and accelerate the transformation of global food systems.
• Approach: The initiative promotes collaboration among governments, tech firms, startups, and farmers to integrate AI, data analytics, and automation into the entire agricultural value chain — from soil health and crop management to logistics and market linkages.

Key Challenges in Global Agriculture

1. Declining Agricultural Workforce

• Massive rural-to-urban migration and an ageing farmer population have led to labour shortages in agriculture.
• As per FAO projections, the global farming workforce could decline by nearly 30% by 2050, threatening productivity and food security.

2. Climate Change and Weather Extremes

• Rising temperatures, erratic rainfall, and frequent droughts are already impacting global yields.
• Without decisive climate action, calorie yields from staple crops such as rice, maize, and wheat could fall by up to 24% by 2100.
• Crop losses from floods, pests, and heat stress are expected to intensify, particularly in tropical regions.

3. Degradation of Natural Resources

• Water Stress: Agriculture consumes about 70% of the world’s freshwater, and 71% of aquifers are facing depletion.
• Soil Degradation: Nearly one-third of global soils are degraded due to overuse of fertilizers, deforestation, and erosion; up to 90% of topsoil may be degraded by 2050.
• Biodiversity Loss: Monoculture cropping and habitat conversion are reducing pollinator populations essential for food security.

These interconnected challenges call for a technological transformation, not only to improve yields but to make agriculture more resilient, efficient, and sustainable.

Use Cases of Deep Tech in Agriculture Highlighted by the Report

1. Intello Labs: AI and Computer Vision for Quality Assessment

• Solution: Fruitsort — an AI and computer vision-based system that analyses fresh produce for quality grading.
• How it Works: Uses high-resolution cameras and machine learning to identify defects, ripeness, and size of fruits and vegetables.
• Impact: Reduces post-harvest losses, improves pricing transparency, and strengthens supply chain efficiency for farmers and retailers.

2. Pradhan Mantri Fasal Bima Yojana (PMFBY): Remote Sensing for Crop Insurance

• Technology Used: Satellite imagery, high-resolution drones, and geospatial analytics.
• Purpose: Enables accurate crop loss assessment and transparent claim settlement, minimizing delays and disputes.
• Supporting Tools: A dedicated mobile application helps farmers register claims and track assessments in real time.
• Significance: Integrates AI and remote sensing into India’s flagship crop insurance programme, enhancing trust and efficiency.

3. Infosys: “5G.NATURAL” Smart Farming Programme

• Focus: Deploying AI-powered robotic swarm systems using 5G connectivity for scalable, modular, and intelligent farm operations.
• Applications: Automated harvesting, pest detection, soil monitoring, and irrigation management.
• Outcome: Reduces dependence on manual labour, increases productivity, and supports precision agriculture.

4. Boomitra: URVARA Project for Carbon Monitoring and Regeneration

• Project: Vital Agricultural Regeneration and Adaptation (URVARA).
• Objective: To monitor, report, and verify (MRV) carbon sequestration in agricultural soils using AI and remote sensing.
• Relevance: Encourages regenerative farming practices that capture carbon, enhance soil fertility, and generate carbon credits for farmers, aligning with India’s climate commitments.

Significance of Deep Tech in Agriculture

1. Enhancing Productivity and Efficiency: AI and robotics automate repetitive tasks like weeding, irrigation, and harvesting, improving efficiency and reducing costs.
2. Climate-Smart Agriculture: Predictive analytics can forecast droughts, floods, or pest outbreaks, enabling proactive adaptation measures.
3. Sustainable Resource Management: Precision farming ensures optimal use of water, fertilizers, and pesticides, reducing ecological impact and input costs.
4. Market Transparency and Farmer Empowerment: AI-driven grading systems and digital marketplaces ensure fair pricing and reduced exploitation by intermediaries.
5. Global Food Security: Integrating deep tech into agriculture contributes to SDG 2 (Zero Hunger) and SDG 13 (Climate Action) by ensuring resilient and sustainable food systems.

India’s Role and Opportunities

India, with its strong IT sector and expanding agritech ecosystem, is uniquely positioned to lead the global AI-in-agriculture movement.

• Initiatives like Digital Agriculture Mission 2021-25, AgriStack, and PMFBY 2.0 already integrate remote sensing, drones, and AI analytics.
• Startups such as CropIn, SatSure, and Fasal are using deep tech to deliver climate advisory, yield prediction, and farm-level decision tools.
• India’s large-scale farmer networks and digital infrastructure provide fertile ground for AI4AI-aligned collaborations.

Challenges Ahead

• Digital Divide: Unequal access to technology and internet connectivity may exclude small and marginal farmers.
• Data Privacy: Agricultural data ownership and protection remain unresolved.
• Skill Gaps: Farmers need training to effectively use AI-based tools.
• Cost Barriers: High initial investments may deter adoption without policy incentives.

Conclusion

The WEF’s AI for Agriculture Initiative (AI4AI) underscores a new paradigm in global food systems, one driven by data, automation, and sustainability.

For India, aligning its agricultural policies with AI4AI’s principles can create a future where technology empowers farmers, boosts productivity, and safeguards natural ecosystems.

As the world faces mounting pressures of population growth and climate change, deep technology in agriculture offers not just efficiency, but a pathway to food security, climate resilience, and rural prosperity.