Predictive modelling.
Integrated pest management. Every spray, a decision.
Pestren is a localized AI system for pest management —
prediction before outbreaks, integrated management during them,
and restoration after. Pesticides used when needed, where needed,
only as much as needed. Built for India first. Designed for the world.
Modern agriculture worked. For a while.
Now the cracks are obvious.
Pesticide residues are everywhere — in food, in water,
in soil and mother's womb. People worldwide are worrying about long-term health risks and the chemistries that came after it.
At the same time, nature is adapting, and condusive climatic conditions are changing for worse. Pests
evolve. What used to work stops working. So farmers spray more. Costs
go up. Productions go down. The pipeline for new chemicals?
Slower and more expensive than ever.
Farmers are stuck in a bad loop.
⬣ The bad loop
Use more chemicals →
get stagnant/reduced production →
pay more →
take on more risk, with poison on our plate.
And yet they can't just stop. If pests win, crops die.
Food prices go up. People starve. It's existential.
For a long time, this looked unsolvable.
Now there is hope.
02 / The Shift
Three things changed at once.
AI vision, lightweight localized models, and farmer-reach channels
all became viable in the same window. Now, we can replace blanket spraying with informed
decisions — at smallholder scale, in local languages.
⬢ FORCE 01
AI can see.
It can identify individual pests in real time. Sensors, satellite data,
and cameras are accessible to put everywhere.
⬢ FORCE 02
Models got localizable.
Training a per-targetted district forecasting model used to cost ₹5–10 lakh
in compute and labeling. With modern lightweight architectures —
gradient-boosted trees plus small transformers for time-series —
it now costs under 5 lakh per district. Localization is
economically viable at scale.
⬢ FORCE 03
Channels finally reach the farmer.
WhatsApp, IVR, FPO shareholder/producer networks, and the 10,000 FPO scheme
together created a distribution layer that didn't exist five years
ago. Insights can travel the last mile — to the small/marginal (1-2 hectare) farmer
who actually needs them, in the language they speak.
03 / The Infrastructure
The Intelligence Inside
for Ag-Enterprises & Governments.
Pestren is a model-infrastructure layer. We don't compete with farm-reach apps; we power them. We provide high-margin API access to the entities that manage agricultural risk at scale via curated domain knowledge.
⬢ FOR ENTERPRISES
Ag-Tech APIs.
Input companies and Ag-retailers use our models to provide precision advisory, moving from selling generic chemicals to selling prescribed advisories.
⬢ FOR INSURERS
Risk Underwriting.
Crop insurers use our predicitons data to underwrite risk accurately and trigger pre-emptive actions that reduce village-wide claim payouts.
⬢ FOR GOVERNMENTS
State Surveillance.
Agriculture departments may integrate our localized weights into their surveillance systems to protect food security and manage pest outbreak load.
04 / The Precedent
The approach is proven.
The localization is missing.
Four of the world's most credible agri-AI programs have validated
the two pillars Pestren is built on — predictive pest modelling
and AI-driven IPM. Each works in its own geography and crop.
None of them is built for the Indian smallholder of varied agro-climatic conditions, end to end,
state by state.
⬢ PRECEDENT 01 · USA
Climate Corporation
PREDICTIVE PEST MODELLING · ACQUIRED BY BAYER · $1B+
Pioneered weather + crop + pest data fusion for outbreak forecasting
across the US corn and soy belt. Proved that predictive modelling
materially reduces yield loss when models are calibrated to local
agroclimatic data.
Gap: trained on broadacre US data — not Indian smallholders, not monsoon-fed paddy
⬢ PRECEDENT 02 · INDIA
Wadhwani AI
COTTON PEST PROGRAM · GOVT OF INDIA PARTNER
Showed that AI pest identification works in Indian field conditions —
farmers photograph traps, the model identifies pink bollworm pressure,
advisory follows. Demonstrated viability in 5 cotton states.
Gap: one crop, one pest, ad-hoc deployment — no replicable district playbook
Built the global benchmark for AI-driven integrated pest management —
pest lifecycle modelling, natural-predator analysis, minimum-effective
chemical recommendations. Reduced input costs in pilots across Latin
America and Europe.
Gap: priced for agribusinesses, not 1-2 hectare farmers and FPOs
⬢ PRECEDENT 04 · INDIA
BioCrop
BIO-IPM · INDIAN FARMER CHANNEL
Proved that biological controls and minimal-pesticide protocols
can be delivered through Indian extension channels at smallholder
scale. Validated the demand side — farmers will adopt IPM when
advisory is local, trusted, and timely.
Gap: no predictive AI layer — reactive, not pre-emptive
⬡ The unfilled space
Predictive modelling and IPM. Localized district by district.
Delivered to Indian smallholders through FPOs and localized stakeholders. That's the gap.
04 / The System
Pestren isn't a notification.
It's the improved pest management, instrumented.
Three phases. One system. Pre-outbreak prediction. During-outbreak
integrated management. Post-outbreak restoration. Each phase runs on
locally-trained models because pests don't respect averages —
and neither do the right interventions.
PHASE 01
Predict.
7-21 days before an outbreak
District-level outbreak forecasts from weather, soil, crop calendar,
and 8+ years of pest surveillance data. Get ahead of the curve, not
chase it.
7-day & 21-day outbreak risk scoring
Per-cluster pest pressure heatmaps
Pre-emptive IPM action plans
Trap & beneficial-insect deployment
PHASE 02
Manage.
During an active outbreak
Integrated pest management in real time — biological controls first,
targeted spot-spray only when the threshold is crossed, scale fixation
across the clusters so infestation doesn't jump fields. Every
intervention is a decision, not a default.
Image-based pest identification & threshold scoring
Biological vs. chemical decision support
Cluster-wide IPM containment protocols
PHASE 03
Restore.
After the outbreak passes
Soil restoration after chemical stress. Yield recovery planning.
Supply-chain damage reporting for compensation. The outbreak ends —
the work doesn't.
Soil & microbiome recovery plans
Yield loss assessment & insurance docs
Next-cropping season prevention adjustments
05 / The Geography
10 states. 10 different fights.
A pan-India model fails because pests aren't pan-India. The whitefly
of Haryana is not the brown planthopper of West Bengal. The fall
armyworm in Karnataka behaves nothing like the locusts of Rajasthan.
Each state needs its own model, trained on its own ground.
01
Andhra Pradesh
Virus outbreaks, weak smallholder monitoring
02
Telangana
Pest infestations, supply-chain losses
03
Karnataka
Fall armyworm epicenter, maize destruction
04
Madhya Pradesh
Locusts, armyworm, low monitoring density
05
Bihar
Poor pest advisory access
06
West Bengal
Humidity-driven fungal/pest outbreaks
07
Odisha
Cyclones, weak post-harvest systems
08
Chhattisgarh
Rice-heavy, limited digital adoption
09
Assam
Flood-driven pest spread
10
Jharkhand
Weak extension networks
INDIA · PEST PRESSURE BY STATE10 / 28
⬡ The thesis
One standardized methodology, deployed locally to every
state — not one model averaged across all of them.
06 / The Edge
Standardized playbook. Localized models.
Other AgriTech ships one model and hopes it generalizes. We ship one
method and train it multiple times. Lower per-district cost. Higher per-district accuracy.
⬢ THE METHOD
One standardized pipeline.
Same architecture, same ingestion stack, same validation
thresholds, same delivery layer — replicable in the same crop season per
district. The playbook is the IP.
Documented from FPO onboarding through outcome feedback.
reduced
Time to deploy a new district
⬢ THE MODEL
Every district gets its own weights.
Local weather pattern. Local pest history. Local soil parameters. Local crop
calendar. Local ground-truth from FPO partners. The model
knows your cluster.
Outcome feedback compounds — every cropping season the model gets
better at this specific land, not generic farmland.
reduced
Training cost per district
⬢ The bet
Healthier food. Safer soil.
Lower the load on the planet. All at once.
Agriculture is one of the biggest markets in the world. If you can
cut costs and increase yields at the same time, adoption isn't slow.
It's explosive.
PHASE 01 · NOW
Pilot launch
2 states · 3 districts · 1 crop systems · the localization playbook proves itself in eastern India
PHASE 02 · 18 MO
State scale-up
12 districts across pilot states · first signed contract
PHASE 03 · YEAR 3
Pan-India
5 states active · 1000 acres under advisory · increased crop coverage
PHASE 04 · YEAR 15+
Global south
SE Asia · Sub-Asia · same playbook, different soils · the Stripe of pest intelligence