Centre for the Fourth Industrial Revolution
Artificial Intelligence for Agricultural Innovation (AI4AI)
C4IR India
Ongoing (updated Feb 2022)
Image: Getty Images/iStockphoto
Transforming the agricultural landscape, in a way that is profitable and sustainable for farmers, using AI. Being Piloted in India and adopted in Saudi Arabia
Impact
- Accelerated technology adoption
- Robust protocols, systems, and processes
- New/updated government policy or regulation
Challenge
Transform the agricultural landscape, in a way that is profitable and sustainable for farmers and supports the rural population that depend on agriculture for their livelihoods
Solution
- Develop frameworks and use cases for the application of AI to Agriculture
- Partner with national and local governments to implement a large-scale set of pilot projects
Outcome
- 11 frameworks and 24 use cases identified to support technology implementation
- Multiple partnerships established to support pilots
- Ongoing pilots and adoption of new AI systems
Integrating advanced AI technologies in agriculture is required to build the scope and pace necessary for a digitally enabled transformation India’s agriculture sector. To capitalize on the potential of emerging technologies for agriculture the AI4AI initiative was founded, in collaboration with the Ministry of Agriculture, the National Institution for Transforming India (NITI) Aayog and the Ministry of Electronics and the Government of Telangana.
Phase I of the project engaged 60 plus partners across government and business to identify the key opportunities for AI across 4 areas: intelligent crop farming, smart farming, farmgate-to-fork, and data driven agriculture. This resulted in key recommendations to advance AI4AI develop India and in other countries such as Columbia.
These recommendations are now being used to implement designed, large-scale pilot projects (with organisations including Digital Green, Technoserve and Dehaat) to realize the value propounded in the major frameworks and use cases developed. Use cases include, among others, sowing window prediction, tillage estimation, crop detection and health monitoring, pest incidence prediction and early prediction of crop yield. System improvements, such as the deployment of an AI-based quality assaying model, have also been introduced as a result of the initiative thus far.
