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Graders and Sorters, Agricultural Products

Occupation · SOC 45-2041.00

Grade, sort, or classify unprocessed food and other agricultural products by size, weight, color, or condition.

Also called: Apple Sorter · Grader · Potato Grader · Sorter · Agriculture Laborer · Corn Lab Technician · Distribution Technician · Egg Grader · Egg Worker · Potato Sorter · Agricultural Establishment Grader Inspector · Agricultural Produce Sorter

Job family: Farming, Fishing, and Forestry Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-45-2041-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

5th-percentile task overlap — yet about 5,100 openings a year (-5.4% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Low 7th -1.4
LLM task exposure, γ (OpenAI / Eloundou) Low 14th 0.1
AI assistant applicability (Microsoft) Low 7th 0.0

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.4 · 44th percentile among occupations · Moderate

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook Declining · -5.4% by 2034
Projected annual openings 5,100
Employment 2024 → 2034 38,900 → 36,800

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international 3 occupations below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

16% mean task exposure (2025)
19th percentile of 427 placed occupations
+0 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Food and Beverage Tasters and Graders · 7515 31% Minimal
Tobacco Preparers and Tobacco Products Makers · 7516 14% Not exposed
Pelt Dressers, Tanners and Fellmongers · 7535 11% Not exposed

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Tasks

All 6 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Production and Processing 3.6
English Language 3.0
Mechanical 3.0
Food Production 2.8
Education and Training 2.7
Foreign Language 2.4
Public Safety and Security 2.3
Administration and Management 2.3

Abilities

Manual Dexterity 3.3
Near Vision 3.3
Category Flexibility 3.1
Oral Comprehension 3.0
Finger Dexterity 3.0
Oral Expression 2.9
Trunk Strength 2.9
Speech Recognition 2.9
Arm-Hand Steadiness 2.8
Speech Clarity 2.8
Problem Sensitivity 2.6
Static Strength 2.6
Multilimb Coordination 2.5
Visual Color Discrimination 2.5
Deductive Reasoning 2.4
Inductive Reasoning 2.4
Flexibility of Closure 2.4
Information Ordering 2.3
Selective Attention 2.3
Dynamic Strength 2.3
Stamina 2.3
Extent Flexibility 2.3
Far Vision 2.3

Essential skills

Monitoring 2.9
Active Listening 2.8
Speaking 2.8
Critical Thinking 2.5
Reading Comprehension 2.3
Writing 2.3

Transferable skills

Coordination 2.5
Judgment and Decision Making 2.3
Time Management 2.3

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Microsoft Excel Spreadsheet software Hot technology In demand
Microsoft Outlook Electronic mail software Hot technology In demand
Microsoft Access Data base user interface and query software Hot technology
Microsoft Active Server Pages ASP Web platform development software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft PowerPoint Presentation software Hot technology
Microsoft Word Word processing software Hot technology

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Spend Time Standing 4.7
Face-to-Face Discussions with Individuals and Within Teams 4.6
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.5
Spend Time Making Repetitive Motions 4.4
Physical Proximity 4.4
Indoors, Environmentally Controlled 4.3
Time Pressure 4.2
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 4.2
Pace Determined by Speed of Equipment 4.0
Work With or Contribute to a Work Group or Team 3.8
Contact With Others 3.6
Importance of Being Exact or Accurate 3.6
Frequency of Decision Making 3.1
Work Outcomes and Results of Other Workers 3.0
Impact of Decisions on Co-workers or Company Results 3.0
Freedom to Make Decisions 2.9
Spend Time Bending or Twisting Your Body 2.8
Dealing With Unpleasant, Angry, or Discourteous People 2.7
Exposed to Contaminants 2.7
Determine Tasks, Priorities and Goals 2.6
Health and Safety of Other Workers 2.6
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.6
Spend Time Walking or Running 2.5
Level of Competition 2.4
Coordinate or Lead Others in Accomplishing Work Activities 2.4
Conflict Situations 2.3
Degree of Automation 2.3
Consequence of Error 2.2
Exposed to Very Hot or Cold Temperatures 2.1
Importance of Repeating Same Tasks 2.0
Telephone Conversations 1.8
Spend Time Climbing Ladders, Scaffolds, or Poles 1.8
E-Mail 1.8
Deal With External Customers or the Public in General 1.8
Exposed to High Places 1.7
Wear Specialized Protective or Safety Equipment such as Breathing Apparatus, Safety Harness, Full Protection Suits, or Radiation Protection 1.7
Indoors, Not Environmentally Controlled 1.6
Outdoors, Exposed to All Weather Conditions 1.6
Outdoors, Under Cover 1.6
Spend Time Keeping or Regaining Balance 1.6

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
No formal educational credential · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

Less than a High School Diploma 63.8%
High School Diploma 26.7%
Associate's Degree (or other 2-year degree) 4.9%
Post-Secondary Certificate 4.7%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Interest areas

Physical/Manual Labor 6.1
Agriculture 3.4
Culinary Art 1.5
Accounting 1.4
Transportation/Machine Operation 1.4
Life Science 1.4
Nature/Outdoors 1.2
Management/Administration 1.2

Career interests (Holland / RIASEC)

Realistic 5.6
Conventional 5.0
Investigative 2.5
Enterprising 2.2
Artistic 1.7
Social 1.5

Work styles

Attention to Detail 2.2
Dependability 2.1

Wages & employment

U.S. · annual wages (BLS OEWS)

$30k10th$34k25th$35kMedian$38k75th$43k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
39k202437k2034 (proj.)-5.4% · Declining
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $30,490
25th percentile $33,550
Median (50th) $35,430
75th percentile $38,010
90th percentile $43,280
People employed 26,870

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Agriculture, Forestry, Fishing and Hunting · Sector 10,710 $34,840
Manufacturing · Sector 10,150 $35,320
Wholesale Trade · Sector 3,320 $37,890
Administrative and Support and Waste Management and Remediation Services · Sector 630 $35,970
Temporary Help Services · National industry 340 $35,060
Retail Trade · Sector 240 $35,290
Transportation and Warehousing · Sector 220 $41,590
Testing Laboratories and Services · National industry 70 $46,970
Professional, Scientific, and Technical Services · Sector — $38,980

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Agriculture, Forestry, Fishing and Hunting · Sector 145.16× 10,710
Manufacturing · Sector 4.56× 10,150
Wholesale Trade · Sector 3.16× 3,320
Temporary Help Services · National industry 0.74× 340
Administrative and Support and Waste Management and Remediation Services · Sector 0.4× 630
Transportation and Warehousing · Sector 0.17× 220
Retail Trade · Sector 0.09× 240

Part of the Agriculture career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Graders and Sorters, Agricultural Products sits at the 5th percentile of AI task-overlap and the 5th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Graders and Sorters, Agricultural Products Packaging and Filling Machine Operators and Tenders Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders Meat, Poultry, and Fish Cutters and Trimmers Weighers, Measurers, Checkers, and Samplers, Recordkeeping AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Graders and Sorters, Agricultural Products — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 19th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Graders and Sorters, Agricultural Products show 5th-percentile AI task overlap — and about 5,100 annual U.S. openings

  • Graders and Sorters, Agricultural Products rank in the 5th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 5,100 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be declining (-5.4%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $35,430, across about 26,870 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Graders and Sorters, Agricultural Products show 5th-percentile AI task overlap — and about 5,100 annual U.S. openings

• Graders and Sorters, Agricultural Products rank in the 5th percentile (Low band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 5,100 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be declining (-5.4%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $35,430, across about 26,870 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Graders and Sorters, Agricultural Products". https://singulariki.com/roles/role-45-2041-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Graders and Sorters, Agricultural Products." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-45-2041-00

APA

Singulariki. (2026). Graders and Sorters, Agricultural Products. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-45-2041-00

BibTeX 
@misc{singulariki-role-45-2041-00,
  title  = {Graders and Sorters, Agricultural Products},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-45-2041-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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