Indian farmers face challenges from fragmented landholdings, which limit economies of scale, and from unpredictable weather, which heightens risk. Limited access to data-driven insights further constrains productivity, market access, and overall farm profitability. Google is stepping in to bring greater precision, scale, and intelligence to the country’s farming ecosystem. Researchers at Google DeepMind in Bengaluru have developed two foundational AI models – Agricultural Landscape Understanding (ALU) and Agricultural Monitoring & Event Detection (AMED) – which utilise satellite imagery and machine learning to provide farmers with data-driven insights. Following their adoption here, Google is expanding their reach to Malaysia, Vietnam, Indonesia and Japan.
These models have been supporting the Indian ecosystem, including startups, central and state government entities, to strengthen India’s agricultural resilience. The APIs, or application programming interface, that help facilitate developers’ access to the outputs of these models, are freely available. These tools help deliver actionable insights – from mapping vegetation and water bodies, to monitoring crop cycles and detecting agricultural events every 15 days.
“We have always believed that solutions that address India’s most pressing challenges can also solve for the world,” said Alok Talekar, lead of agriculture and sustainability research at Google DeepMind. “These two agri AI models, which were first built to strengthen India’s agricultural resilience, will now also support agricultural sustainability across the Asia-Pacific region, within just months of these models’ India-first releases,” he told FE.
“The Indian ecosystem’s use cases have delivered to our ambition for AI to assist targeted, data-driven action and solutions that benefit stakeholders across India’s agriculture landscape. Building on this momentum, we recently expanded access for both ALU API and AMED API to trusted testers in Malaysia, Vietnam, Indonesia, and Japan,,” Talekar said.
According to Talekar, currently, most large-scale solutions usually target big farm owners, and don’t address the needs of smallholders, who produce more than one-third of the world’s food, but are at the frontlines of growing climate threats. “Our goal is to support the development of solutions that help smallholder farmers, most of whom are also in the Global South,” he said.
How ALU and AMED deliver actionable farm insights
ALU identifies fields, water bodies, and vegetation boundaries, while AMED delivers critical field-level insights on the most cultivated crops, and their sowing and harvest timelines at individual field levels. AMED also refreshes data actively, approximately every 15 days, helping detect agricultural events in individual fields throughout a country’s agricultural landscape.
India-first use cases
Talekar said the Indian ecosystem has demonstrated a variety of use cases for these models towards strengthening the resilience of the local agri sector. Krishi DSS, an integrated agri-insight and decision-making platform being developed by Amnex for the government’s department of agriculture and farmer welfare, is leveraging the ALU and AMED APIs to power advanced analytics for crop health monitoring, acreage estimation, irrigation advisories, and climate impact assessment.
TerraStack, incubated at IIT-Bombay, has built a rural land intelligence system that can support rural lending, land record modernisation, and help determine vulnerability of farms to climate risk. The system uses the ALU API to identify farm boundaries and detect potential encroachment and changes in land ownership, which is a crucial factor in the support farmers avail from both the public and private sector.
Vassar Labs plans to integrate the APIs into its existing fieldWISE platform and data stacks, offering a comprehensive climate-smart agriculture platform. Serving over one crore Indian farmers through several state projects, the integrations will enhance its existing solutions for different terrains and cropping patterns – from crop and field monitoring for agriculture departments, to personalised advisories on crop, irrigation, pest, and fertiliser management, as well as market and pricing dynamics for farmers.
Sugee.io, which aims to democratise financial access for rural communities, is integrating insights from ALU API directly into its loan origination system towards improving efficiencies in the application process for the farmers, while also supporting the quality, reliability and compliance of agricultural loans for banks.
The Council on Energy, Environment and Water (CEEW) plans to use the ALU and AMED APIs to develop first-of-its-kind, high-resolution analysis that will help identify regions that can benefit most from crop diversification. This foundational insight will enable CEEW to conceptualise a new mechanism for direct, responsive and differentiated income support to farmers, nudging them to grow more nutritious and climate-friendly crops. CEEW is also integrating the models into its Climate Data Platform, which will be available as a digital public good for guiding targeted state-level interventions.
Additionally, the government of Telangana’s Agricultural Data Exchange (ADeX) platform, which connects agricultural data users and providers via an open network, also offers the output of these models to support the development of agricultural solutions by the ecosystem that positively impact the state’s approximately 6 million farmers and the broader agricultural chain.
