← Risk register SOC 45-2041 · reviewed 2026-08-11

Graders and Sorters, Agricultural Products

25,180 US workers · median $35,730/yr · Agriculture

COOKED

The core task — visually inspecting produce, eggs, or meat on a line and pulling out defects by size, color, ripeness, or blemish — is the textbook case for optical sorting machines, and machine vision plus air-jet and robotic ejectors already do it faster and more consistently in large packinghouses. Physical presence on a wet, cold, fast-moving line is a real moat against pure software, but here the competing technology isn't a chatbot, it's a camera rig that's been commercially mature for two decades and keeps getting cheaper. What survives is hand-grading of delicate or irregular commodities, machine tending and calibration, and USDA-standard grade calls where a human still signs the certificate.

10-year outlook: Employment keeps shrinking as optical sorting spreads down to mid-sized packers; the remaining jobs cluster around machine calibration, QA sampling, and specialty crops that bruise.

US employment, 2019–2025-26.7%
34,34025,180 workers

This fall is concentrated in 2020 and has not recovered since.

Median pay $25,670 → $35,730 +11.4% in real terms (nominal +39.2%, less ~25% US inflation over the period)

The job count is not the verdict

This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.

So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.

BLS projection, 2024–2034

-5.4%

Percentage only. The projection counts a different population from the 25,180 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.

Exposed, and shrinking

Both signals point the same way: the tasks are largely automatable and the BLS projects -5.4% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.

Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.

~5,100 openings a year on average, including replacing people who leave.

One email if this score changes. Watch as many occupations as you like from the same address — no account, and nothing is sent on a schedule, only when a verdict actually moves.

Also known as — 24 job titles this covers

Titles reported by people doing this work, from the US Department of Labor's O*NET survey. If your job title is here, this page is about your work even though the name doesn't match.

GraderSorterEgg GraderEgg SorterEgg TesterEgg TrayerEgg WorkerNut CullerNut GraderNut PackerNut PickerNut SorterOnion TierRing FacerBean SorterBulb GraderCarrot TierCelery TierChick SexerClam SorterEgg CandlerFood TasterHide GraderLeaf Sorter

Score — 24/100 resistance

Holding it up: embodiment (12/20). Weakest point: liability shield (1/20).

Five dimensions, 0–20 each, summed. Higher means more protected. The arithmetic is shown so you can check it: 5 + 12 + 1 + 2 + 4 = 24. · Scored 2026-08-11, and re-examined when evidence accumulates rather than on a schedule.

Task resistance 5/20

Core tasks are already automatable Judging a tomato by color break or an egg by candling shadow is exactly what a hyperspectral camera and weight cell do at 20 fruits per second, and the USDA grade standards you apply are written as numeric tolerances — defect counts, diameter ranges, brix — which is a threshold rule a sorting machine executes without fatigue, leaving only the 5 points for hand-handling of soft berries, stone fruit, and odd-shaped commodities that bruise in a chute.

Embodiment 12/20

Some physical or field component You stand at a wet, 38-degree line pulling culls at speed, reaching into bins and flipping fruit to check the blossom end, which is why this isn't a 2 — but the work happens inside a packinghouse with fixed lighting, a fixed belt height, and product presented to you, so it's nowhere near the 16-plus of a field crew or a livestock handler working uncontrolled ground.

Liability shield 1/20

No licence, no signature requirement There is no licence to grade produce; USDA-licensed graders under the Agricultural Marketing Act are a separate, much smaller federal classification, and the packinghouse sorter works under a plant's own quality program with the shipper, not the sorter, answering to the buyer for a mis-graded load.

Trust premium 2/20

Anonymous artifact production The buyer in Chicago sees a grade stamp and a pack date on a carton, never your name, and the lot moves on identical terms whether you or the person on the next shift pulled the culls.

Judgment & accountability 4/20

Executes defined procedures on defined inputs Your calls are bounded by a posted grade sheet and physical sizing rings — U.S. No. 1 versus No. 2 on a specified percentage of surface blemish — and a borderline lot gets kicked to the line lead or QC supervisor rather than decided by you, though setting the ripeness cutoff on the first pass of a new block earns the points above zero.

This occupation has already been through one. Headcount fell 36.7% between 2017 and 2025 — 39,810 to 25,180 — while the median wage held roughly flat in real terms (+ 17.4% after inflation). A job being commoditised usually loses pay along with headcount. One that shrinks by half while pay holds is leaving a specialist core behind, and the score above was assigned from the occupation title without sight of that history — so it may be describing the job this used to be rather than the people still doing it. Why this is a known limit.

Confidence: high · reviewed 2026-08-11 · how scoring works

What this job involves — and which parts are yours

The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.

AI already does these at usable quality

These still need a person

Active moats on the surviving side: embodiment

How to future-proof this job

Training paths for your skill gaps: MIT OpenCourseWare — finance and accounting free · edX — supply chain and inventory management free to audit · edX — operations management and process monitoring courses free to audit · Coursera — people management and team leadership specialisations free to audit · Coursera — customer service and client-facing skill courses free to audit · Coursera — work planning and personal productivity free to audit · Coursera — communication and interpersonal skills free to audit · OSHA Outreach Training — the 10- and 30-hour cards most employers ask for low · Apprenticeship.gov — industrial maintenance and millwright programs paid to train · Apprenticeship.gov — paid, employer-sponsored trade apprenticeships, searchable by trade and ZIP paid to train

All 35 skills ranked by how many jobs they open →

Where this experience transfers — occupations you could move toward

Computed from U.S. Dept. of Labor O*NET skill and knowledge profiles: high overlap with what you already do, a materially higher resistance score, no large jump in required training, and no licence you would have to start a new pipeline to get. Targets that pay meaningfully less, that are themselves COOKED, or whose own headcount is falling are excluded — a move into a shrinking trade is not an escape.

Cooks, Restaurant EXPOSED · 57/100 · you already have ~85% of the skill profile

Skills to close: Management of Financial Resources, Management of Material Resources, Operations Monitoring, Management of Personnel Resources

Food Preparation Workers EXPOSED · 41/100 · you already have ~84% of the skill profile

Skills to close: Service Orientation, Time Management, Management of Personnel Resources, Social Perceptiveness

Dishwashers EXPOSED · 38/100 · you already have ~81% of the skill profile

Skills to close: Operation and Control, Equipment Maintenance, Operations Monitoring, Repairing

What would move this back up — beyond any one person

The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 38/100 — EXPOSED.

4 specific changes that would raise this score
  • already happening task resistance +4

    Two-tier shift: as optical sorters absorb size/color/blemish culling, the surviving day is calibration of sorter thresholds against USDA grade standards, lot sampling and certificate work, and hand-grading of soft or irregular commodities (table grapes, stone fruit, tobacco leaf) that machines still bruise. Watch for job postings retitled toward 'grading technician / QA sampler'

  • plausible liability shield +5

    USDA AMS official grading certificates (7 CFR Part 51/70) still require a licensed federal or federal-state licensed grader to sign; if AMS formally rules that camera-derived grade data cannot substitute for a licensed grader's certification on export lots or on shell-egg grading (where an AMS grader's presence is already tied to the USDA grade shield), the signing role becomes a legally protected niche

  • plausible judgment accountability +3

    Food-safety recall exposure: if FSMA 204 traceability enforcement (compliance date now 2026-2028) makes lot-level accept/reject calls the documented decision point for a recall, the person making borderline lot dispositions owns a consequential call under ambiguity rather than just pulling culls

  • plausible embodiment +2

    Little upward route in large packinghouses, but growth in field-pack and small-lot organic/direct-market operations where capex on a $300k optical line never pencils out keeps hand grading on wet, irregular, variable-throughput lines

The limit. Ceiling is low. The displacing technology is a mature camera rig, not a model, so capability arguments do not help; and buyers of graded produce pay for the USDA grade shield, not for a human eye, so there is no realistic trust-premium route. Even with every lever, this is a much smaller occupation doing a narrower certifying job.

These are conditions, not forecasts — what would have to happen, not what will. Specific rules, cases and bills are named so you can go and check whether they exist and where they stand; verify before relying on any of them. Nothing here is legal or financial advice.

Where this work is, and what it pays there

BLS metro figures for 42 areas. The verdict above does not change by city — the rubric judges what the work involves, not where it happens — but pay and headcount do, and the national median hides a very wide range.

Most of these jobs

Yakima, WA 1,250 $34,750 -3%
Fresno, CA 950 $35,010 -2%
Bakersfield-Delano, CA 720 $35,310 -1%
Visalia, CA 540 $34,960 -2%
Salinas, CA 520 $37,340 +5%
Wenatchee-East Wenatchee, WA 460 $35,080 -2%
Fayetteville-Springdale-Rogers, AR 420 $36,000 +1%
Kennewick-Richland, WA 390 $35,720 +0%

Best paid

Grand Forks, ND-MN 40 $48,700 +36%
San Francisco-Oakland-Fremont, CA 80 $46,350 +30%
Sacramento-Roseville-Folsom, CA 210 $44,510 +25%

Percentages are against this occupation's national median of $35,730. Counts are jobs in that metro, not vacancies. Metros where the BLS suppressed the cell are absent rather than shown as zero.

Who is actually doing this — nobody, on the record

We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.

That is worth saying out loud next to a score of 24. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.

Read that as a gap in the reporting we can see, not proof of absence — the dispatch runs on English-language feeds and misses plenty. If you know of a case, tell us, or add a field report from inside the job.

Quick take — do you do this job?

Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.

Self-reported and unverified — a sentiment signal, not a survey. One response per person per occupation; you can change your answer.

Field reports — what people say has changed

No field reports yet. A written account takes a paragraph rather than a tap, goes to an editor before it appears, and is the one thing on this page the rubric cannot produce on its own.

File a field report

Concrete beats general: a tool that arrived, a task that moved, a headcount decision you watched happen. Don't include anything that identifies you or your employer if that would put you at risk.

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