Office of the Principal Scientific Adviser (OPSA) released ‘Future Farming in India: AI Playbook for Agriculture’ | Current Affairs | Vision IAS
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In Summary

The OPSA's 'Future Farming in India: AI Playbook' promotes AI adoption in agriculture through frameworks, key use cases, and addressing challenges like limited tech exposure, fragmentation, and investment barriers. 

In Summary

This has been released along with two other publications under the AI for India 2030 initiative led by the Centre for the Fourth Industrial Revolution (C4IR) India, World Economic Forum (WEF). 

  • Launched under guidance of OPSA and Ministry of Electronics and Information Technology (MeitY), AI for India 2030 initiative aims to develop frameworks placing responsible, inclusive, and scale-driven AI at the heart of India's digital economy.

Key Highlights of Report

  • Potential AI use cases in Agriculture: 
    • Intelligent Crop Planning: Uses wide range of data such as soil health, weather patterns, historical prices and food import/export trends for recommending optimal crops. 
    • Smart Farming: Satellite crop monitoring, decision support systems, rapid soil health analysis, pest prediction, hyperlocal weather advice, yield prediction, automated farm machinery etc. 
    • Farm-to-Fork Solutions: Ensuring quality and traceability, optimizing supply chain, fintech adoption, market linkage demand and price prediction etc.
  • Framework for developing AI ecosystems in Agriculture: Report presents Inclusive Multistakeholder Pathway for the Accelerated Convergence of AI Technologies (IMPACT AI) Framework. It has three pillars: 
    • It has three pillarsEnable (formulate AI strategy, enable DPI for AI, AI upskilling etc.), Create (develop innovative AI products, establish AI sandbox etc.) and Deliver (empower front line extension systems, AI marketplace, creating awareness etc.).

Challenges in AI Adoption for Agriculture

  • Limited Exposure to Technology: Fewer than 20% of Indian farmers use digital technologies. 
  • Lack of financial capability: Low income of Indian farmers restricts both their ability and willingness to pay for AI solutions. 
  • Fragmentation: Close to 85% of India’s 150 million farmers are smallholders and the Indian farmer’s average landholding is just 1.08 hectares. 
  • Lack of Investment: Development and use of AI solutions need investment in infrastructure and resources. 
  • Perception of Risk: There are very limited institutional mechanisms for validating technology before it is deployed. 
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