We're a Noida-based AI plant intelligence platform — combining species matching, growth prediction, and MRV-ready project delivery for carbon programmes, NGOs, and land restoration teams across India.
India has committed to restoring 26 million hectares of degraded land by 2030 under its NDC, and agroforestry is central to meeting that target. Yet the projects being deployed to deliver it — carbon programmes, NGO-led plantation drives, government restoration schemes — are failing at a system level.
The failure is not a shortage of investment or intention. It's a data problem. Wrong species chosen for the local soil and rainfall. Fertilisation inputs that don't account for soil carbon building. Aftercare schedules that are abandoned after planting day. And no structured record to support monitoring, reporting, or verification.
Gro2Green was built to fix this — starting with the AI layer that makes every project decision defensible and data-backed, then delivering it on the ground through a hands-on operation based in Noida.
AI-assisted and on-ground assessment before any species is committed to.
Organic inputs matched to build long-term soil health — the foundation of carbon permanence.
ML-scheduled management that adjusts as actual growth data comes in.
Every project generates structured records supporting monitoring, reporting, and verification.
The policy and market conditions for large-scale agroforestry have converged — the execution gap is what remains.
India's nationally determined contribution commits to restoring 26 million hectares of degraded land by 2030. Agroforestry is the primary land-use pathway to get there.
Article 6-aligned carbon markets, domestic carbon credit frameworks, and voluntary market demand are creating real revenue pathways for well-documented agroforestry and reforestation projects.
Funding and policy are ahead of delivery capacity. Projects that can demonstrate high survival rates, verifiable sequestration, and credible MRV records will define the first generation of successful Indian carbon forestry.
Every capability is purpose-built for the specific decisions in agroforestry and carbon plantation — not generic horticulture software.
Foundation models generate context-aware species mixes from site conditions, objectives (carbon / livelihoods / restoration), rainfall, soil type, and local market access.
Image recognition identifies existing vegetation, land use patterns, soil surface condition, and shade structure — the site characterisation that underpins additionality assessment.
Models trained on real plantation outcomes predict species-level survival probability and carbon sequestration trajectories before planting is committed — de-risking design decisions.
Watering and feeding schedules generated post-planting and updated from growth check-in data — adaptive management that improves survival rates in the critical first two years.
Every project generates geo-tagged species records, planting logs, survival measurements, and carbon estimates — structured for compatibility with Verra, Gold Standard, and national MRV frameworks.
Site coordinates enrich design decisions with district-level rainfall, temperature, and humidity data — so recommendations reflect local climate reality, not textbook averages.
The same AI platform applies whether the project is a community agroforestry block or a multi-district carbon programme.
AI-designed species mixes, survival prediction, and MRV-ready records for carbon project developers and verifiers.
Agroforestry design for livelihood and restoration outcomes, with structured data for impact reporting and donor accountability.
Large-scale AI-planned plantation for campuses, municipalities, and government restoration schemes.
Multipurpose tree and crop integration — AI species selection, soil nutrition, and adaptive management support.
Operating across Noida, NCR, and partner delivery networks for large-scale agroforestry and carbon plantation projects pan-India.
Tell us your land, scale, and objectives — we'll come back with an AI-assisted design and project outline.
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