For India, agriculture is critical. Roughly half of its population depends on agriculture for its livelihood, and the country has the second largest arable land area in the world. As a lead producer of products like rice, wheat, cotton, sugar, and dairy, India’s agricultural system is essential not only to itself, but also to the rest of the world.

However, India’s agriculture system is facing serious challenges. More efficient crop yield is necessary to continue feeding India’s 1.4 billion people. Climate change disrupts our agricultural systems, and at the same time, unsustainable farming practices exacerbate climate change through significant greenhouse gas emissions, water usage and deforestation. Without change, food and environmental systems across the world are at risk.

Agricultural sustainability

According to the Google blog, two teams at Google, AnthroKrishi and Google Partner Innovation, are leveraging AI to tackle this challenge, aligned with Google’s AI Principles. The teams’ goal is to progress agricultural sustainability, beginning with India. 

The team is working on a whole body of AI-powered technologies to organize and utilize India’s agricultural data, the most foundational of which is developing a unified “landscape understanding.”

Landscape understanding leverages satellite imagery and ML to draw boundaries between field, the basic unit of agriculture and essential in creative meaningful insights. With field segments established, the model can determine the acreage of farm fields, forest and woodland areas, and can identify irrigation structures like farm wells and dug ponds to build tools for drought preparedness.

Landscape Monitoring

The research team is also developing “landscape monitoring” models, which provide a more detailed picture of an individual field’s current performance and future needs. Future landscape monitoring models would be able to determine data like crop type, field size, distance to water and a crop’s last sow or harvest date. The team also hopes to provide in-depth data about farm pond- information like water availability over the past month, year, or three years would be critical in establishing water security and drought management strategies.

Farming lifecycle

The impact of AI on agriculture is evident across various stages of the farming lifecycle. In precision farming, AI-powered technologies enable farmers to analyse soul health, optimise irrigation, and tailor fertiliser usage based on specific crop requirements.

Crop monitoring and disease detection represent another realm where AI is making substantial inroads. By leveraging AI algorithms on satellite unary and drone data, farmers can monitor crop health in real-time, enabling early detection of diseases and pest infestations. The ability to intervene promptly enhances crop resilience, minimises losses, and reduce the reliance on chemical interventions. 

AI also plays significant role in optimising water usage, a critical concern in water-scarce regions. AI-powered systems analyse climatic data, soil moisture levels and crop water requirements to optimize irrigation schedules. The result is reduction in water wastage, increased water use efficiency and enhanced crop yields.

Conclusion

According to Google, field data is key to unlocking the potential of India’s agricultural power — with a deep and accurate understanding of field performance and ever-changing environmental conditions, farmers can reduce land and water waste while increasing their crop yield. However, the impact of these insights extend well beyond individual farmers and empower India’s entire agricultural ecosystem. With more information on farm performance and needs, agricultural loans become more accessible and state governments can support several farming districts at scale. This information also supports India’s rapidly growing agricultural technology industry, as new technologies are developed to make farming practices more efficient and sustainable.