Quality assessment and grading of onions are often subjective and vary across procurement centers, resulting in disputes and inconsistencies.
Ministry of Consumer Affairs, Food & Public Distribution
Good pick. Genuinely. Now sit down, because the judges are going to try anyway — and this is what they will try.
Strong pick. Almost everything here favours you — cheap props, self-built data, an invisible theme label and a judge-verifiable result — but the description defines nothing, so go and find the actual NAFED grading specification and build to that instead of to thresholds you chose. Roughly 160–370 teams are expected to go here.
The receipts
Every red flag on this statement, in full. These are the four places it bites.
Exhibit A
Internal rot is invisible from outside and is precisely the defect that causes procurement disputes, so a purely external vision system cannot detect the failure mode that matters most — say this before a judge says it to you
It gets worse
Estimating the percentage composition of a whole lot from a photograph of its surface is a sampling problem, not a vision problem, and a heap's top layer is not a random sample of the sack
Still reading?
Undersized is a physical measurement and pixel counts mean nothing without scale, so a reference object in frame is not optional — without it your size class is fabricated
And the finisher
Grade A and URS are never defined in the statement, so if you invent thresholds a procurement officer will simply say your grades are not the ones they use
The damage report
Every score this statement earned, and what each one actually costs you.
Feasibility
4/5Actually buildable, which on this slate is rarer than it sounds. Do not squander it on scope.
This is one of the few statements where you can build your own dataset in an afternoon — onions cost almost nothing, the four defect classes are externally visible, and you can photograph hundreds of labelled bulbs under varied lighting yourself rather than hunting for a corpus that does not exist.
Innovation scope
4/5There is something genuinely new here. Do not bury it under another dashboard.
The description is five bullets with no method, no architecture and no interface prescribed, so how you segment a heap, how you handle occlusion and how you turn per-bulb results into a defensible lot percentage are all yours to design.
Clarity
2/5Nobody is sure what is being asked, quite possibly including the people who asked it.
Under 350 characters with no background or description section at all — it never defines what Grade A or URS actually mean in millimetres or defect tolerance, never states an accuracy target, and never says whether you are grading a sample or a whole lot, which is the difference between a vision problem and a sampling problem.
Acceptance potential
4/5Strong footing before you have written a line. Try not to waste it.
A genuine hidden gem — physical props you can carry in, a dataset you can create yourself, a result a judge verifies by looking, and the portal has filed onion grading under Fitness and Sports so nobody browsing Agriculture or FoodTech will ever find it.
Effort
MediumManageable — which means the bar for polish just went up, because you have no excuse left.
A detection model, a size calibration step, an aggregation rule and a report-generating mobile app is a contained build with one model and one screen, which is genuinely modest by the standards of this portal.
Demo-ability
EasyEasy to demo — and so is everyone else's. Working is the floor here, not the achievement.
You can put real onions on the table and let a judge photograph them, and because the defects are visible to the naked eye the judge can immediately check whether your grading is right — verification in the room is worth more than any interface.
Data
None suppliedNo dataset comes with this one, so every accuracy figure you quote is a number about labels you invented.
Nothing is provided with the statement. You are sourcing, cleaning and labelling it yourself, and that work is invisible in the demo but very visible in the questions.
The demo they will have already seen
Somewhere around 160–370 teams are heading here, and the description is doing the choosing for most of them. They will read the same brief, reach the same architecture, and build a version of the same demo you are planning. Being correct is the floor. If your five minutes could be swapped with the team before you and nobody in the room would notice, you have not picked badly — you have built predictably, which costs exactly the same and hurts more.
What survives
The ground worth standing on when the questions start.
- The Fitness and Sports theme label is about as wrong as it gets, so this statement is effectively invisible to anyone browsing by theme
- You can build your own labelled dataset from a sack of onions and a phone, which removes the data dependency that kills most vision statements
- Physical props mean the judge participates in the demo rather than watching it, and a correct grade they can verify themselves is far more persuasive than a reported accuracy number
Nothing here is fatal. It is just the list of places this statement pushes back, and you now get to push there first.
The framing is a joke. The findings are not — they are the same analysis on the statement page, and every line above is attached to a score or a fact in the record. It is one opinion with its reasoning attached, so argue with it before you trust it.