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Manual wheat grading vs phone-photo image analysis: what each does well.
Manual grading with sieves and a trained eye is the reference. Image analysis can make the visible checks faster and more consistent, but it does not replace a lab.
| Manual grading | Phone-photo image analysis | |
|---|---|---|
| Who can do it | A trained grader | Anyone following the capture guide |
| Equipment | Sieves, balance, trays | A phone, a mat and a scale marker |
| Consistency | Varies by person and shift | Same method for every photo |
| Visible defects | Yes | Yes |
| Moisture, protein, gluten | Need instruments | Not visible; moisture entered from a meter |
| Hidden insects | Need splitting or lab tests | Not visible |
| Record | Often a paper slip | Saved to the lot with a shareable report |
| Basis | Usually weight | Count and size; weight basis needs the scale and grain weights |
When to use which
- Image analysis: fast screening at a gate or mandi, consistent first numbers both sides can see, and a history by lot.
- Manual or lab: borderline lots, disputes that need a reference method, and anything a camera cannot see.
- Both: use the photo for most lots and keep comparing it with lab results to know how far to trust it.
What to ask of any image-based tool
- Does it show a range on each number, or a bare decimal?
- Does it refuse bad photos, or grade anything?
- Can it apply your own spec, not only one fixed standard?
- Has it been compared with hand counts on samples like yours?
How GrainGrade does it
GrainGrade turns one phone photo of a wheat sample into a grain count, sizes in millimetres, defect percentages and the FCI FAQ grade, with a range on every number instead of a bare decimal. The camera coaches the shot first (level, a scale marker in frame, focus and light) and refuses a bad photo with a reason in Hindi or English rather than analysing it into a wrong answer. Seven defect classes are checked, grading rules are data so a mill’s or exporter’s own spec can sit beside FCI FAQ, and every sample is saved to its lot with a shareable PDF or WhatsApp report. GrainGrade is in development: counting is built and measured; the other figures on the product page are design targets. Moisture is not visible to a camera and is entered from a meter reading.
Questions
Is image-based wheat grading as accurate as manual grading?
For visible defects it can be consistent and fast, but accuracy depends on capture and on testing against hand counts. Moisture, protein and hidden insects still need instruments.
Can an app replace a grain lab?
No. It can screen most lots quickly and consistently; borderline lots and non-visible parameters still need a lab or a meter.
See it on your own data. GrainGrade — Wheat graded from a photo. Book a 30-minute working session with an engineer.