• Google’s inaugural regional accelerator will support 16 organizations applying AI to biodiversity, agriculture, carbon markets and climate resilience.
• Participants will receive three months of technical support, specialist AI models and mentorship, although Google has not disclosed direct funding.
• The cohort reflects rising demand for reliable environmental data, particularly in carbon verification, nature monitoring and smallholder agriculture.

Google has selected 16 organizations for its inaugural DeepMind Accelerator: AI for the Planet in Asia-Pacific, expanding the use of artificial intelligence across some of the region’s most pressing environmental challenges.

The three-month program began this week with a hands-on bootcamp in Singapore. Its participants include startups, nonprofit organizations and research teams from Australia, India, Indonesia, Japan, New Zealand, Singapore, South Korea and Thailand.

Their projects span biodiversity monitoring, regenerative agriculture, disaster-risk prediction, urban energy management and carbon-credit verification. Participants will gain access to Google’s AI technology, technical guidance and expert mentorship.

Google said the accelerator will help organizations navigate technical barriers and scale their environmental solutions. No direct investment or grant funding was disclosed.

Building better data for nature

Six participants are developing tools for conservation and climate resilience.

New Zealand’s 800 Trust and Listening Lab are using bioacoustics to monitor biodiversity through sound. Wildlife.ai, also based in New Zealand, is building open-source, AI-powered cameras for wildlife conservation.

Singapore-based Kumi Analytics combines remote sensing and deep learning to create environmental baselines for conservation projects. In South Korea, TelePIX is converting satellite observations into intelligence for global mangrove monitoring.

Indonesia’s Yayasan Ekosistem Lestari is taking a risk-focused approach. Its predictive platform aims to connect environmental degradation with exposure to disasters.

These tools could improve how governments, investors and conservation groups measure ecological change. Stronger baseline data is increasingly important as nature-related reporting develops and scrutiny of biodiversity claims intensifies.

Targeting smallholder agriculture

Five projects focus on farming, where climate volatility and limited access to agronomic services continue to constrain rural economies.

Edufarmers in Indonesia provides smallholder farmers with near real-time guidance on pests, diseases and weather through commonly used messaging applications. Thailand’s Living Roots uses data and AI to design biological fertilizers for specific crops.

Singapore-based SIGMA is developing satellite models to estimate crop yields and strengthen climate resilience. Terrastack in India combines satellite and agronomic information to produce plot-level land intelligence.

Australia’s X-Centric is pursuing a hardware-based solution. Its portable, AI-enabled X-ray equipment is designed to provide immediate geochemical analysis without relying on conventional soil laboratories.

For agricultural businesses and lenders, such systems could improve yield forecasting and risk assessment. They may also broaden smallholder access to carbon markets and climate-linked finance, provided data quality and verification standards remain robust.

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Testing carbon-market integrity

Five organizations will apply AI to emissions reduction, carbon removal and urban energy systems.

Japan’s Archeda uses satellite data to make nature-based carbon credits measurable at scale. India’s Climitra Carbon applies geospatial AI to verify invasive-species removal and conversion into biochar.

Farmers for Forests uses AI-powered drones to measure carbon and biodiversity outcomes from smallholder agroforestry in India. Varaha Climate combines remote sensing and AI to verify regenerative agriculture and carbon removal, allowing farmers to earn carbon credits.

Singapore’s City Syntax Lab is developing an agentic AI platform to optimize energy use and carbon emissions across urban districts.

The commercial stakes are substantial. Carbon markets need credible monitoring, reporting and verification to attract institutional capital and withstand regulatory scrutiny. AI can reduce measurement costs, but governance will remain critical. Model transparency, data ownership and independent assurance will determine whether these systems produce trusted environmental assets.

What executives and investors should watch

The cohort offers a view of where environmental AI is moving in Asia-Pacific. Much of the activity is concentrated on measurement, verification and decision support rather than speculative consumer applications.

That focus matters for companies facing climate disclosures, supply-chain risks and nature-related obligations. Better environmental intelligence could strengthen investment decisions and reduce project-development costs.

Yet access to advanced models alone will not guarantee impact. Participants must demonstrate reliable performance across diverse landscapes and communities. They must also protect sensitive ecological and agricultural data.

If those challenges are addressed, the accelerator’s projects could help build the digital infrastructure needed for credible climate finance and environmental governance across Asia-Pacific.