Skip to content
man bot

Elementary occupations · code 9211

Crop farm labourers

Crop farm labourers perform simple and routine tasks on farms in the production of crops such as fruit, nuts, grains and vegetables. Tasks include - (a) digging and shovelling to clear ditches or for other purposes; (b) loading and unloading supplies, produce and other materials; (c) raking, pitching and stacking straw, hay and similar materials; (d) watering, thinning and weeding crops by hand or using hand tools; (e) picking fruit, nuts, vegetables and other crops; (f) planting and harvesting field crops such as rice, by hand; (g) grading, sorting, bunching and packing produce into containers; (h) performing minor repairs on fixtures, buildings, equipment and fences. Examples of the occupations classified here: - Cane planter - Fruit picker - Rice farm labourer - Vegetable picker Some related occupations classified elsewhere: - Skilled farm worker (field crops) - 6111 - Construction labourer (building work) - 9313 - Firewood collector - 9624 - Water collector - 9624

None of this job's tasks scored high enough to count. It ranks 5 of 427 for holding up against AI.

Also known as

  • vineyard worker
  • fruit and vegetable picker

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

90.9
Holds up
against AI Not Exposed

AI could take on 9.1 of 100, on average
so it holds up at 100 − 9.1 = 90.9

That's the whole sum, and you can check it against the ILO's study.

69
How hands-on
out of 100

From O*NET, matched to one US job

38.4
How fiddly
and cramped

finger skill 42.9 · hand skill 51.7 · cramped spaces 20.5 · averaged

Kind of hands-on work · hands-on 50+ · fiddly under 39.4 Hands-on, less fiddly work

Less fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 69 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 12.5, while the average is 9.1. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  1. 12.5

    Grading, sorting, bunching and packing produce into containers

    Very Low
  2. 12

    Performing minor repairs on fixtures, buildings, equipment and fences.

    Very Low
  3. 9.9

    Loading and unloading supplies, produce and other materials

    Very Low
  4. 9.3

    Picking fruit, nuts, vegetables and other crops

    Very Low
  5. 8.5

    Watering, thinning, and weeding crops by hand or using hand tools

    Very Low
  6. 7.5

    Digging and shovelling to clear ditches or for other purposes

    Very Low
  7. 7.3

    Raking, pitching and stacking straw, hay and similar material

    Very Low
  8. 6.3

    Planting and harvesting field crops, such as rice, by hand

    Very Low

8 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.7-work-setting

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.8. This one is above that.