The global agrifood sector continues to face significant pressure. While an estimated 2.1 billion people – over 25% of the world’s current population – were food insecure in 2025, global food production must increase by up to 50% to support 9.7 billion people by 2050. The agriculture ecosystem accordingly needs to develop targeted interventions to address present and future food security concerns.
Two AI models built by Google DeepMind have been helping support this effort. Agricultural Landscape Understanding (ALU) and Agricultural Monitoring & Event Detection (AMED) use satellite imagery to map field boundaries and track agricultural activity. Originally built to support India’s ecosystem, the outcomes of models have been shared with trusted testers in 11 countries across Asia Pacific and Africa. The models’ outputs have been made freely available to the ecosystem as APIs and on Google Earth. The ALU data layer is one of the most popular layers on Google Earth globally.
The ecosystem has been leveraging these models’ outputs to build solutions that strengthen both the Indian and global agriculture sector, ranging from improved sustainable cultivation, to increased farmer access to credit, and stronger policy decision making and resource management.
● Growing sustainability of food production: CarbonFarm has used the ALU API and Gemini to automate field-level insights, especially field-level delineation, in programs that aim to reduce the environmental impact of rice cultivation. This is part of CarbonFarm’s efforts to support 2 million hectares of low-carbon rice by 2030.
● Strengthening farmer access to agri credit: Using ALU and AMED APIs, Terrastack has built a spatial intelligence platform that has mapped over 140 million hectares of farmland, reducing the need for physical field visits. This is enabling India’s agri ecosystem to make faster and more accurate decisions that benefit farmers.● Supporting India’s DPI for Agri: Telangana’s ADEX platform is leveraging ALU and AMED as part of its efforts to support innovations that benefit the state’s 5 million+ farmers. This includes a pilot of the state’s Krishivaas application, which generates actionable, hyperlocal advisories on crop stress, crop-specific weather patterns and localized pest outbreaks.
● Scaling digital agri infrastructure to the world: The new geoAI4stats initiative at the UN Food and Agriculture Organization (FAO), which has received support from Google.org as part of the AI Collaborative: Food Security, plans to integrate ALU and AMED into FAO’s global CROPGRIDS data repository to strengthen monitoring for agricultural sustainability.
● Strengthening water management: Karnataka’s Water Resources Department has combined ALU and AMED with localized weather and remote sensing data to strengthen dynamic water management across the state’s 2.6 million hectares of irrigated area.
Remarking on the deployment of the models’ capabilities, Alok Talekar, Lead, Agriculture and Sustainability Research, Google DeepMind, who leads the AnthroKrishi team stated: “Our AnthroKrishi team has been dedicated to supporting targeted agricultural solutions that both increase farm productivity and reduce climate impact. The growing application of our India-first AI models’ APIs to impact-focused solutions – ranging from farmer credit, to crop advisory and policy decision-making – across both the Indian and global ecosystem encourages us in our approach. As these models expand to support even more countries, we look forward to the immense potential they will unlock for key global priorities, from food security to agricultural resilience.”
As adoption continues to grow across sectors and geographies, Google and Google DeepMind’s AnthroKrishi team stay committed to providing data-driven insights and supporting a more productive, resilient, and sustainable future for the Indian and global agricultural ecosystem.
Additional quotes from partners
“Over 100 million farming households remain underserved because there is no reliable way to understand what is happening at the farm level. Google’s ALU and AMED models have enabled us to build a spatial intelligence platform that is helping transform fragmented land, crop, and income data into actionable intelligence for every farm in India. This creates the foundational infrastructure that enables lenders, insurers, governments, and agribusinesses to make better decisions, expand access to services, and strengthen the resilience of India’s agricultural economy.” – Aaryan Dangi, Co-founder and CEO, Terrastack
“For years, the biggest barrier to scaling sustainable rice cultivation wasn’t knowing how to reduce methane emissions — it was measuring, verifying, and paying for those reductions at scale. By using Google’s ALU model to map individual field boundaries, enabling satellite-based verification of farming practices, and Gemini to give farmers real-time feedback as they implement water management techniques, we can now measure adoption, water outcomes, and methane emission reductions. This data helps farmers adopt climate-resilient techniques while enabling outcome-based incentive programs built on trusted, verifiable results.” – Aparna Raturi, Chief Operating Officer, CarbonFarm
“Historically, our planning relied on aggregate and largely descriptive datasets, making it challenging to understand water productivity at a local level. By integrating Google’s ALU and AMED models with KWRIS, localized weather and remote sensing data, field observations, and community participation, we have gained precision and timely crop intelligence. This enables us to identify areas requiring productivity improvements and supports more informed decisions to improve water productivity and sustainable water resource management across our river basins.” – Advanced Centre for Integrated Water Resources Management (ACIWRM), Water Resources Department, Government of Karnataka.
“One of the biggest challenges in agriculture is access to quality data. With ADeX, we set out to create a shared platform that Telangana’s government departments, universities, startups, and partners can build upon. By integrating Google’s ALU and AMED datasets, we are enabling capabilities such as field boundary delineation, crop stress analysis, early warning systems, and hyperlocal advisories. Together, these innovations are helping us create a stronger digital foundation for agricultural services and innovation across Telangana” – Information Technology, Electronics and Communications (ITEC) Department, Government of Telangana
“Better agricultural decisions start with better data. By combining AI, geospatial intelligence, and statistical systems, geoAI4stats will help countries access more timely and granular agricultural insights. With support from Google.org, we are bringing together advanced AI capabilities and FAO’s agricultural expertise to strengthen agricultural data as a global public good. This will help countries generate better insights for agricultural planning, sustainability efforts, and food security interventions while accelerating the transformation of agri-food systems.” – Francesco Tubiello, FAO Senior Statistician and geoAI4Stats Project Lead
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Author: Shivam
Shivam Dwivedi is a senior journalist with extensive experience in research-driven journalism, policy communication, and multi-platform storytelling. His areas of interest include international relations, defence, science & technology, education, urban development, agriculture, spirituality, and environmental sustainability. His work focuses on in-depth analysis, public discourse, and impactful narratives across governance and development sectors, with a strong commitment to the Sustainable Development Goals (SDGs). Contact: [email protected]
