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Wadhwani AI CottonAce partner programs

Verified evidenceThreshold is the control

AI-counted pink-bollworm traps and threshold-based spray advisories

Farmers can spray against a shared trap-count threshold instead of a calendar or subjective visual assessment.

Agriculture · Karnataka, Maharashtra, and later multi-state India

An editorial scene for Wadhwani AI CottonAce partner programs contrasts inspect fields and decide pesticide timing from experience, visible damage, or calendar practice. with checks pheromone traps weekly and uploads smartphone images. in the ai-counted pink-bollworm traps and threshold-based spray advisories workflow.

Executive brief

The operating-model shift, in one view.

This case is valuable because the larger later test was null: AI advice creates value only when the targeted risk is present and the human sensing routine is sustainable.

AI value · Farmer income in initial field evaluations

Verified

Up to 22% higher income in the first-year evaluations; no significant benefit in the 2021-2022 multi-state experiment

The mixed evidence is essential: early evaluations were not all randomized, and unusually high rainfall with low pest pressure eliminated significant benefit in the later multi-state experiment.

Before

Inspect fields and decide pesticide timing from experience, visible damage, or calendar practice. → Apply pesticide, sometimes before or after the economically effective pest threshold.

After

Checks pheromone traps weekly and uploads smartphone images. → Detects and counts pink bollworm moths and compares counts with an action threshold. → Receive a localized spray advisory and decide whether and how to apply treatment.

Human boundary

The model determines whether the observed count crosses an advisory threshold; farmers retain the pesticide application decision.

Why it matters

Farmers can spray against a shared trap-count threshold instead of a calendar or subjective visual assessment.

How the work changed

Before

How the work ran before the change.

  1. Step 1 of 2

    Cotton farmer

    Inspect fields and decide pesticide timing from experience, visible damage, or calendar practice.

    ControlFarmer judgment

  2. Step 2 of 2

    Farmer

    Apply pesticide, sometimes before or after the economically effective pest threshold.

    ControlAvailable pesticide and labor

What changed

Farmers can spray against a shared trap-count threshold instead of a calendar or subjective visual assessment.

Decision rightHuman sets the threshold; AI decides each instance

After

How the same work runs now.

  1. Step 1 of 3

    Lead farmer

    Checks pheromone traps weekly and uploads smartphone images.

    ControlManual trap placement and image capture

  2. Step 2 of 3

    CottonAce

    Detects and counts pink bollworm moths and compares counts with an action threshold.

    ControlIntegrated-pest-management threshold

  3. Step 3 of 3

    Farmers

    Receive a localized spray advisory and decide whether and how to apply treatment.

    ControlFarmer retains application authority

Process model built from the published workflow evidence for Wadhwani AI CottonAce partner programs. Every step, actor, and control appears in full below.
Every step, actor, and control

Exception path

Farmers and extension partners can withhold treatment, inspect manually, or use other integrated-pest-management methods; rainfall and low pest pressure can make the advisory produce no measurable benefit.

Work removed

  • Subjective manual moth counting for every participating farmer
  • Some prophylactic or mistimed pesticide applications

Decision authority

The model determines whether the observed count crosses an advisory threshold; farmers retain the pesticide application decision.

Before

  1. 01

    Cotton farmer

    Inspect fields and decide pesticide timing from experience, visible damage, or calendar practice.

    Control: Farmer judgment

  2. 02

    Farmer

    Apply pesticide, sometimes before or after the economically effective pest threshold.

    Control: Available pesticide and labor

After

  1. 01

    Lead farmer

    Checks pheromone traps weekly and uploads smartphone images.

    Control: Manual trap placement and image capture

  2. 02

    CottonAce

    Detects and counts pink bollworm moths and compares counts with an action threshold.

    Control: Integrated-pest-management threshold

  3. 03

    Farmers

    Receive a localized spray advisory and decide whether and how to apply treatment.

    Control: Farmer retains application authority

Work that left the path

  • Subjective manual moth counting for every participating farmer
  • Some prophylactic or mistimed pesticide applications

Human role before

Farmers individually diagnosed pest pressure and timed spraying.

Human role after

A lead farmer supplies trap images; the model standardizes counting; farmers act on threshold advisories and still perform field work.

AI roleComputer-vision detection and counting of moths in pheromone-trap images used to trigger localized pest advisories.

Outcomes

Farmer income in initial field evaluations

Verified

Non-adopter or control farmers in 2020-2021 evaluation locations→Up to 22% higher income in the first-year evaluations; no significant benefit in the 2021-2022 multi-state experiment

2020-2022 · Initial evaluations in Ranebennur and Wardha followed by a seven-state experiment

The mixed evidence is essential: early evaluations were not all randomized, and unusually high rainfall with low pest pressure eliminated significant benefit in the later multi-state experiment.

What leaders can reuse

Anti-pattern

Promoting the 22% first-year result without the later null result or ignoring the labor required to inspect traps.

Questions

  1. 01What external condition determines whether the intervention can help?
  2. 02Is the human data-collection burden sustainable?

Portability conditions

  • Reliable local image capture
  • Validated economic action thresholds
  • Weather- and pest-aware evaluation

Reputation risk

medium

Evidence and authority

What the public record supports.

Watch · updated

Watch status: verify the cited source and deployment condition before reusing this case.

1 independent, 1 peer reviewed; publication outcomes are verified.

Bundle 1.0.0 · reviewed 2026-08-23 · stable ID 968b26cd2d0b5f01

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Sources

Read the evidence, freshness, caveat, and version policy.