1 · Crop quality grading (computer vision)

When a farmer uploads photos of a lot, OpenCV separates the produce from the background and measures four independent attributes:

• Colour — how much of the produce falls inside the healthy hue range for that crop, and how vivid it is.
• Uniformity — circular standard deviation of hue; a consistent lot grades higher because buyers pay for consistency.
• Defects — dark-spot and bruise ratio found by adaptive thresholding inside the produce mask.
• Size — contour analysis of individual units and how consistent they are.

These are fused into a 0–100 score and a Grade A / B / C verdict. A blurred or badly lit photo caps the score instead of inflating it, and the farmer is told to re-shoot.

2 · Price recommendation

The suggested price starts from the median modal price of the last 45 days in the farmer's own district and is then adjusted by four multiplicative factors:

price = district_base_price
        × grade_factor      (A +18%, B 0%, C −18%)
        × season_factor     (lean-supply month +12%, harvest glut −12%)
        × demand_factor     (from the demand index)
        × premium_factor    (organic +15%, verified certificate +8%)

Every factor is shown to the farmer with its reason, so the number is never a black box. The band width comes from that crop's historical volatility — wide for tomato, narrow for wheat.

3 · Demand prediction

A 0–100 demand index fuses four independent signals:

• Price momentum — last 30 days against the 90 days before.
• Supply pressure — falling mandi arrivals with firm prices means tight supply.
• Season — the crop's own lean/glut calendar.
• Marketplace pull — open buyer requirements against available listings on AgriVision itself.

A least-squares trend over the weekly means, modulated by seasonality, gives a six-week price forecast with a widening confidence band.

4 · OCMR certificate verification

OCMR — Optical Certificate & Mark Recognition — is the six-stage pipeline that reads a quality certificate and decides whether to trust it:

1. Image preparation: denoise, deskew, adaptive binarisation.
2. Tesseract OCR with per-word confidence.
3. Field extraction by regular expression — number, issuer, dates, holder, crop, grade.
4. QR / barcode detection and cross-check against the printed number.
5. Error Level Analysis to flag digitally edited documents.
6. A ten-check rule engine producing a confidence score.

Expired certificates, wrong-crop certificates and unreadable scans are rejected outright; borderline cases are queued for manual review rather than silently passed.