OpenCV 5 · Agentic analysis

Smarter crop monitoring — powered by computer vision

CropSense AI combines OpenCV 5 computer vision and agentic analysis to help identify visual crop stress, monitor field health, and support earlier decisions.

Visual assessment only — CropSense AI does not diagnose specific diseases.

north-plot-a / IMG_0412.jpgillustration
Aerial maize field with computer-vision annotationsR1 · 4.8%R2 · 2.1%R3 · 1.6%
Vegetation Suspicious region Analysed region

How it works

From photo to field decision in four steps

  1. 01

    Upload

    Drop a JPG, PNG, WEBP or MP4 captured in the field or by drone.

  2. 02

    OpenCV 5 Analysis

    Quality check, vegetation segmentation, contour detection and measurements.

  3. 03

    Agent Decision

    The agent reviews results and decides whether to look closer or request a new image.

  4. 04

    Assessment

    A visual crop-health assessment with regions, measurements and next steps.

Capabilities

Built for agronomists, not demos

Computer Vision

Excess-green segmentation, HSV thresholds, contour extraction and sharpness metrics.

Field Monitoring

Organise analyses by field and crop, with health status tracked over time.

Visual Anomaly Detection

Suspicious regions highlighted with bounding boxes, contours and affected-area %.

Agentic Analysis

An agent loop that requests region re-analysis or human review when needed.

Historical Comparison

Compare each analysis with the field's previous result to spot change early.

Reports

Weekly and monthly field reports, ready for export.

Agentic vision

Not image → fixed result. Perception → decision → action → new perception.

Step 4 of 6

Agent decision

The agent can use computer-vision results to determine whether additional analysis is required.

Technology

A clean, service-oriented stack

OpenCV 5
Vision engine
Python
Processing
FastAPI
REST service
AI Agent
Pluggable provider
PostgreSQL
Analysis store
AWS
Deploy target

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