---
title: >-
  Wadhwani AI CottonAce partner programs: AI-counted pink-bollworm traps and
  threshold-based spray advisories
slug: cottonace-pest-advisory
stable_id: 968b26cd2d0b5f01
company: Wadhwani AI CottonAce partner programs
function_code: agriculture
pattern_codes:
  - threshold_as_control
evidence_strength: verified
publication_tier: showcase
freshness: watch
reviewed_at: '2026-08-23'
updated_at: '2026-08-22'
source_quality_summary: 1 independent, 1 peer reviewed; publication outcomes are verified.
caveat_summary: >-
  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.
collections: []
bundle_version: 1.0.0
bundle_fingerprint: sha256:c23c6cc2b88153d008ea8fda928f632ce0011fc2d4c5036672a16e5d895bab93
canonical_url: https://brianletort.ai/transformations/cottonace-pest-advisory
---

# Wadhwani AI CottonAce partner programs: 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.

Function: Agriculture. Patterns: Threshold as the human control. Evidence: verified.

Freshness: watch. Reviewed: 2026-08-23. Updated: 2026-08-22.
Watch status: verify the cited source and deployment condition before reusing this case.

Source quality: 1 independent, 1 peer reviewed; publication outcomes are verified.

## Before

1. **Cotton farmer** — Inspect fields and decide pesticide timing from experience, visible damage, or calendar practice. (control: Farmer judgment)
2. **Farmer** — Apply pesticide, sometimes before or after the economically effective pest threshold. (control: Available pesticide and labor)

## After

1. **Lead farmer** — Checks pheromone traps weekly and uploads smartphone images. (control: Manual trap placement and image capture)
2. **CottonAce** — Detects and counts pink bollworm moths and compares counts with an action threshold. (control: Integrated-pest-management threshold)
3. **Farmers** — Receive a localized spray advisory and decide whether and how to apply treatment. (control: Farmer retains application authority)

## Decision rights

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

## 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.

## 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. 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.

## Executive lesson

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.

## Anti-pattern

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

## Questions for leaders

- What external condition determines whether the intervention can help?
- Is the human data-collection burden sustainable?

## Sources

- [Artificial Intelligence & Nature-Based Solutions in Agriculture: A BT Cotton Pest Management Case Study in India](https://doi.org/10.32388/nfgp2f) — Qeios
- [Pest management in cotton farms: an AI-system case study from the global South](https://www.kdd.org/kdd2020/accepted-papers/view/pest-management-in-cotton-farms-an-ai-system-case-study-from-the-global-sou.html) — ACM KDD 2020