AI for low-pesticide agriculture

Spray less.
Earn more.
Prove what changed.

Innosapien turns satellite, field and crop data into continuous intelligence—helping farmers act earlier, use fewer inputs and produce more valuable crops.

Open live demo Explore the platform

Built in the field with smallholder farmers

Green chilli plants observed through a crop intelligence system

Plot 04 · Green chilliVegetative stage

Today's recommendation08:40

No pesticide spray needed

Black thrips risk remains below action threshold.

Model confidence

91%

Water stressWatch

Crop healthStable

01 From a single leaf to an entire growing region

Selected field outcomes

Measured through deployed programs in India

100,000+farmer interactions

21%lower pesticide use

61%higher yield per acre

165%higher income per acre

The problem

Farm decisions are still made after the damage is visible.

By the time pest, disease or water stress is obvious, the cheapest and safest options are often gone. Farmers respond with blanket sprays because the field lacks a reliable early-warning system.

Signals arrive too late

Crop stress begins days before it can be diagnosed by eye.

Data stays fragmented

Satellite, weather, field notes and farm records rarely meet in one decision.

Outcomes go unproven

Partners cannot reliably connect an intervention to yield, quality or climate impact.

The Innosapien platform

One intelligence layer.
From leaf to landscape.

A continuous crop-intelligence system that senses what is happening, predicts what comes next and records whether the intervention worked.

Observe

Satellite, cameras, field observations, weather, soil and IoT create a continuous view of the crop.

Leaf → plot → landscape

Understand

A living digital twin turns fragmented signals into crop condition, growth stage and field-level context.

Multimodal state model

Predict

AI anticipates water stress, black thrips, leaf curl and disease risk before damage becomes obvious.

Earlier, more precise decisions

Act + verify

Teams receive minimum-intervention recommendations and a defensible record of what changed.

Advice → action → evidence

Built for the realities of smallholder agriculture: fragmented plots, low connectivity, local languages and uneven digital literacy.

Bring intelligence to your growing region

Interactive — try itSelect Field view, RGB frame or Thermal frame.

Real deployment · Dahanu

The intelligence starts with someone who knows the field.

Jitendra, an Innosapien field scout, uses our plant phenotyping device while inspecting green-chilli crops. The system captures close-range visual and spectral-response evidence, including thermal imagery, and stores each observation for model development.

Field scout: Jitendra
Crop: Green chilli
Location: Dahanu, India

Actual implementation imagery. Sample frames are shown as captured and are not presented as standalone agronomic diagnoses.

Jitendra, an Innosapien field scout, inspecting a green chilli field in Dahanu with a plant phenotyping device

Dahanu · Green chilliField inspection

01 · On-ground inspectionJitendra moves through the crop

The plant phenotyping system keeps observation hands-free while the field scout inspects green-chilli plants in context.

Field scoutMultimodal captureAI databaseModel learning

The product

A living model of the field—not a one-time diagnosis.

Innosapien keeps the crop state current through the season, so each recommendation uses what happened before and improves what happens next.

Live digital twinPalghar cluster · Plot 04

Kharif · Day 47

WWeather

SSoil

CCanopy

Composite crop stateHealthy 87

Updated 14 minutes ago

SentinelMobile imageryWeatherSoilScout

Early warning

Know what the crop needs before damage spreads.

Continuous risk models identify water stress, black thrips, leaf curl and disease pressure early enough to preserve low-input options.

Minimum intervention

Recommend the smallest action that can work.

Field teams move from calendar-based blanket spraying to crop-stage, plot-level decisions with clear thresholds and confidence.

Outcome verification

Connect each decision to an economic outcome.

The same evidence trail records input use, crop response, yield, quality and—where relevant—carbon and nature outcomes.

Interactive — try itOpen the scan, then drag to rotate and pinch or scroll to zoom.

Research plot · spatial twin prototype

Rotate the tree.
Inspect what the model sees.

A real mango-tree scan becomes a navigable spatial record—then a surface for structure, canopy and visual-screening models. This is one capture on the path to a living, time-series crop twin.

Interactive reconstructionOrbit enabled

Mango tree reconstructed as a three-dimensional Gaussian Splat scan

Loads here—then drag to rotate and zoom

Field captureMulti-view video

3D reconstructionGaussian splats

CV layersStructure + screening

Temporal twinRepeated captures

Actual research capture · Mango · 07 Mar 2025

The reconstruction is real. Analytical previews are clearly separated from production disease diagnosis and calibrated physical measurements.

Proof from the field

PalgharMaharashtra, India

251 farmers. 27 villages. One production system rebuilt around evidence.

In a NABARD-supported green-chilli program, Innosapien combined crop intelligence, field delivery and market-quality protocols. Farmers received decisions they could act on—not raw data they had to interpret.

Explore the 251-farm demo

Green chilliSmallholdersLow-pesticide

Reported program outcomes~85% adoption

61%higher yield per acre

21%lower pesticide use

15.8%lower cultivation cost

165%higher income per acre

Selected outcomes reported across the deployed program; field performance varies by season and farm conditions.

One core technology

Start with the crop.
Scale with the system.

Our wedge is low-pesticide agriculture. The underlying intelligence layer extends to other high-value decisions without becoming a collection of unrelated products.

Product wedge01

Green chilli

Low-pesticide production

Early warning and minimum-intervention guidance for water stress, black thrips, leaf curl and residue-sensitive production.

Risk score · spray decision · action log

Expansion02

Sugarcane

Crop and harvest intelligence

Plot-level crop vigour, irrigation stress and harvest-window intelligence for farmers and mills.

Stress map · crop stage · harvest window

Verification layer03

Agroforestry

Landscape outcomes

Geospatial evidence for tree survival, growth, canopy change and community-owned climate programs.

Field evidence · change detection · dMRV

The verification layer

Agriculture is the product.
Verification is the multiplier.

Carbon and dMRV are not a second company. They are an adjacent outcome layer built on the same farm evidence: what changed, where, when and with what result.

Farm decisionEvidence capturedOutcome unlocked

Use fewer spraysRisk score + input logLower cost · residue assurance

Improve crop managementSeason-long crop recordYield · quality · traceability

Restore farm landscapesGeospatial change evidenceCarbon · nature finance

20,400+tCO₂e delivered through nature-based work

420,000+tCO₂e in the project pipeline

One evidence architecture across agronomy, traceability and climate outcomes.

Why Innosapien

The moat compounds with every field and every season.

Competitors typically sell a sensor, a satellite layer, an advisory app or a carbon dashboard. Innosapien connects observation, prediction, intervention and verification in one learning system.

Multimodal farm model

A spatial-temporal model that combines crop imagery, satellite, soil, weather, IoT and field records—not another single-signal dashboard.

Proprietary field data loop

Models improve through real deployments, observed interventions and end-of-season outcomes across crops and growing conditions.

Last-mile operating system

Designed for fragmented plots, intermittent connectivity, local languages and delivery through scouts, farmer groups and partners.

Patented sensing lineage

Years of research in AI, imaging and wearable sensing provide a technical foundation that predates the current precision-agriculture cycle.

SatelliteCrop imagingIoT + weatherField recordsEdge AIDigital twin

Built for deployment

For organizations accountable for farm outcomes.

FPOs & cooperatives

Deliver consistent crop decisions across fragmented plots while retaining farmer trust.

Agribusiness & food companies

Improve supply quality, reduce residue risk and see crop conditions before procurement.

Governments & development partners

Run agricultural programs with measurable adoption, productivity and income outcomes.

Carbon & nature programs

Add audit-ready field evidence to community-led regenerative agriculture and agroforestry.

The company

Frontier technology, built with the people who work the land.

Innosapien grew from research in AI, imaging, sensors and wearable computing—and from a family connection to farming. We build in the field because the last mile is not a distribution problem to solve later; it is part of the product.

Founder & CEO

Sarang Nerkar

Engineer and researcher in AI, sensing and wearable computing. University of Toronto ECE; former Research Scientist at the Humanistic Intelligence Lab; AI course tutor at Oxford.

Product · AI · field systems

Co-founder

Palakshi Nerkar

Public policy and international development leader focused on climate, institutions and scale. Master in Public Policy, Harvard Kennedy School.

Strategy · partnerships · scale

DPIIT recognizedIARI Pusa incubatedAtal New India ChallengeWorld Bank Ag Observatory Top InnovatorCES Innovation Award

Build with us

One crop. One region.
One outcome worth proving.

We partner with producer organizations, agribusinesses, governments and funders to turn a focused field problem into a scalable intelligence system.

Discuss a pilot India today · Global South next