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Crop Disease Spray Decision Support

Detects crop disease and related field health threats from sensor, imaging, and geospatial data to guide targeted spraying, fungicide trial evaluation, field scenario testing, and traceable response actions across farm operations.

The Problem

“Crop disease detection and precision spray decision support for field operations”

Organizations face these key challenges:

1

Manual scouting is slow, inconsistent, and expensive at field scale

2

Disease symptoms, weed pressure, and abiotic stress are easily confused in early stages

3

Machine, imagery, weather, and geospatial data are fragmented across vendors and formats

4

Spray decisions are often blanket recommendations with poor spatial precision

Impact When Solved

Reduce blanket spraying by converting field-wide treatment into zone-, section-, or nozzle-level actionImprove disease and weed detection speed using imagery and sensor-driven monitoringIncrease ROI of fungicide and herbicide programs through field-specific recommendationsSupport digital twin scenario testing for irrigation, pest pressure, and treatment timing

The Shift

Before AI~85% Manual

Human Does

  • •Walk fields and review imagery, machine logs, and weather reports to spot disease, weeds, or stress
  • •Judge threat severity by field and decide broad spray or fungicide actions
  • •Track treated versus untreated trials in spreadsheets and compare outcomes manually
  • •Coordinate operators on where and when to spray and record actions after application

Automation

    With AI~75% Automated

    Human Does

    • •Approve spray recommendations, fungicide actions, and trial plans for each field
    • •Review flagged exceptions where disease, weed pressure, or abiotic stress is uncertain
    • •Set treatment priorities, operational constraints, and traceability requirements across farms

    AI Handles

    • •Monitor imagery, sensor, weather, and geospatial inputs to detect disease, weeds, and related field threats early
    • •Score severity by zone, generate geolocated spray maps, and trigger selective spray or section-control recommendations
    • •Track detections, treatments, and outcomes in a traceable event history across field operations and handoffs
    • •Simulate field scenarios and compare treated versus untreated results to recommend timing and product strategies

    Operating Intelligence

    How Crop Disease Spray Decision Support runs once it is live

    AI runs the first three steps autonomously.

    Humans own every decision.

    The system gets smarter each cycle.

    Confidence89%
    ArchetypeRecommend & Decide
    Shape6-step converge
    Human gates1
    Autonomy
    67%AI controls 4 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapeconverge

    Step 1

    Assemble Context

    Step 2

    Analyze

    Step 3

    Recommend

    Step 4

    Human Decision

    Step 5

    Execute

    Step 6

    Feedback

    AI lead

    Autonomous execution

    1AI
    2AI
    3AI
    5AI
    gate

    Human lead

    Approval, override, feedback

    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Real-World Use Cases

    Geolocated spray decision mapping and section control

    AI watches the field, marks where weeds are located on a map, and helps the sprayer turn sections on or off in the right places.

    Sensor fusion and spatial decision support built on computer vision outputs.practical adjacent workflow built on the same edge vision stack.
    10.0

    Low-/No-Cost Food Supply Chain Traceability Challenge Solutions

    Create affordable digital tools that let food companies track where food came from and where it went, so contaminated products can be found faster.

    Entity tracking and event-chain reconstruction across supply-chain handoffsproposed and early-market; fda is soliciting and showcasing solutions rather than describing a single standardized deployment.
    10.0

    Radio-frequency and AI-based rapid HPAI diagnostic device

    Create a fast test device that uses radio-frequency sensing plus AI to tell if poultry may have highly pathogenic avian influenza.

    signal classificationproposed diagnostic innovation funded by aphis; field readiness still to be proven.
    10.0

    IntelliSense selective weed spraying on Guardian front-boom sprayers

    Cameras on the sprayer look ahead in the field, detect weeds and crop conditions, and turn specific nozzles on or off so chemicals are sprayed only where needed.

    Real-time computer vision classification and rule-based actuation for selective sprayingcommercializing now as a factory-offered system for model year 2026 machines with retrofit support for 2023+ units.
    10.0

    Field-scale treated-vs-untreated fungicide trial for drought-stressed corn

    A farmer sprayed fungicide on one clearly marked part of a corn field and left another part unsprayed, then compared harvest results to see if the spray really helped.

    Controlled experiment and outcome measurementmature agronomic trial method with field-proven deployment; not an advanced ai system in the source.
    10.0
    +3 more use cases(sign up to see all)

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