Gro2Green uses computer vision, large language models, and machine learning to turn site conditions into precise planting decisions — and keep plants alive long after delivery day.
Delhi-NCR has some of the most ambitious urban plantation targets in India — and one of the lowest project success rates.
Plants are chosen from standard vendor lists, not site conditions. Indoor species in full sun, moisture-loving trees in drought-prone soil. The default is failure.
Fertiliser is applied at fixed intervals on fixed rates. No one accounts for NCR's highly variable soil chemistry across districts or the growth stage of each plant.
After planting day, maintenance is left to whoever is nearby. Without adaptive schedules tied to real growth data, most plantation projects plateau at 50–60% survival.
We built an AI platform to fix each of these — not as features bolted onto a plant shop, but as the operational core of how Gro2Green works.
Each module addresses a specific failure point in the greening pipeline — from site intake to long-term aftercare.
A customer or site team uploads one or more photographs of a planting location. The computer vision pipeline identifies shade zones (deep shade, partial, full sun), flags visible soil quality indicators (compaction, moisture, organic matter), detects existing vegetation that will compete for resources, and estimates available planting area.
A structured site profile — shade map, soil condition score, vegetation inventory, estimated canopy gap — that feeds directly into the Plant Matching module. Customers get a plain-language summary; our team gets structured data for the recommendation pipeline.
Object and scene detection — vegetation types, built structure, soil surface, water presence.
Site photo storage and versioning across the full project lifecycle.
Photo uploaded → analysis triggered → structured site profile returned automatically.
The site profile from Module 1 is combined with structured metadata — NCR district microclimate data, current season, customer purpose (ornamental / edible / commercial landscaping / carbon project), budget range, and maintenance commitment level — and passed to a foundation model.
The model generates a ranked plant recommendation list, explaining the reasoning for each species: why it matches the shade map, why the soil profile suits it, how it performs in NCR's summer heat or winter frost, and what companion species will support it.
A ranked species list with survival probability estimates, a companion planting map, and a fertiliser protocol recommendation — in plain language for the customer and structured data for the fulfilment team.
Context-aware recommendations grounded in site data and horticultural knowledge.
NCR microclimate data and project history used as retrieval context for every recommendation.
Serves the AI Advisor widget on the homepage and internal team tooling in real time.
Models trained on historical plantation outcome data — species, site conditions, fertiliser protocols, aftercare cadence, and survival and growth measurements — predict two things: (1) the survival probability of each species in the recommended configuration before we commit to planting, and (2) a week-by-week aftercare schedule that adapts as growth check-in data comes in.
Pre-planting: a risk-ranked species list so our team and the customer can make informed substitutions before sourcing. Post-planting: a dynamic aftercare schedule delivered to the customer and field team — updated automatically after each growth check-in.
Trained on real NCR plantation outcomes — estimates species survival probability before planting begins.
New project outcome data continuously improves model performance — the platform gets smarter with every project.
Field measurements, check-in photos, and schedule adherence records per project — the model's memory.
Each project Gro2Green completes generates structured outcome data: what was planted, where, under which conditions, with which aftercare, and how it grew. This feeds back into our training pipelines — improving survival probability estimates, sharpening aftercare scheduling, and expanding species performance data across NCR's varied microclimates.
This is the compounding advantage of building AI-native from day one: the platform becomes more accurate with every planting, not just more experienced.
Photos, measurements, and metadata structured at intake.
LLM matches species; ML models predict outcomes.
Growth check-ins log real outcomes week-by-week.
Automated pipelines update prediction accuracy continuously.
Every capability is mapped to a specific decision in the planting process, not infrastructure convenience.
LLM-powered recommendation for plant type and fertiliser — contextualised to your site conditions and purpose.
Image recognition pipeline for shade mapping, soil indicators, and existing vegetation detection from site photos.
ML model estimating species survival probability from site conditions before a single plant is sourced.
Site coordinates enriched with NCR district-level microclimate, rainfall, and temperature data to sharpen recommendations.
ML-generated watering and fertilisation schedules, updated from growth check-in data rather than fixed calendars.
Optimal NPK ratio, form, and timing recommended from plant type, soil chemistry, and current growth stage.
Species-level carbon sequestration estimates and survival-weighted projections for NGO and carbon credit teams.
Every project's site data, species selection, and growth outcomes stored and fed back to improve model accuracy over time.
The problem Gro2Green is solving is large, measurable, and unmet.
NCR's urban green cover sits far below WHO recommendations of 9 sq m per person. Hundreds of crores are spent annually on plantation drives — with a fraction of the survival rates that justify the investment.
By applying AI to the three failure points — species mismatch, generic fertilisation, abandoned aftercare — Gro2Green can structurally improve plantation survival rates. More trees survive. Green cover grows. The investment returns value.
Carbon project implementers and NGOs need verified species data, survival rates, and carbon sequestration estimates. Our AI platform generates structured project records that support monitoring, reporting, and verification out of the box.
The same AI platform scales to every use case — what changes is the data input and the output format.
AI-matched plants for balconies, lawns, and common green belts.
Lobby greenery and landscaping with AI-scheduled maintenance.
Large-scale AI-planned plantation programmes.
Structured project data, survival tracking, and carbon sequestration estimates for MRV and impact reporting.
Four stages of AI capability — the first is live, the rest are in active development.
LLM-powered plant and fertiliser recommendation wizard. Running in production on the homepage.
Computer vision pipeline for shade mapping and soil indicator detection from uploaded site photos.
ML model trained on NCR plantation outcomes — pre-planting risk scoring per species.
Dynamic ML-scheduled aftercare that updates from growth check-ins, plus carbon sequestration estimates for project teams and NGOs.
Get in touch for an AI-assisted site assessment, a free plant recommendation, or a full AI-planned project quote.