Precision Agriculture Technicians

Life, Physical, and Social Science Occupations

O*NET-SOC code: 19-4012.01

Apply geospatial technologies, including geographic information systems (GIS) and Global Positioning System (GPS), to agricultural production or management activities, such as pest scouting, site-specific pesticide application, yield mapping, or variable-rate irrigation. May use computers to develop or analyze maps or remote sensing images to compare physical topography with data on soils, fertilizer, pests, or weather.

Data source: BLSPublished: 2026-08
High· relative
LowFour relative bandsVery high
Group-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
Source dataset (XLSX download)
Data source: AnthropicPublished: 2026-03
0.006
0.000Range of values carried here0.745
Group-level value
Scale, basis and source
Observed exposure index, 0–1 as published
Mapped onto O*NET tasks
Published per SOC 2018 occupation; every O*NET occupation with the same SOC 2018 code carries this value
Source dataset (CSV download)
Data source: ILOPublished: 2025
0.26
0.09Range of values carried here0.70
Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
Published per ISCO-08 unit group. Linked to this occupation, wholly or in part, by applying U.S. Bureau of Labor Statistics crosswalks (ISCO-08 to 2010 SOC, 2010 SOC to 2018 SOC) as published
Source dataset (PDF download)

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Show 20 hidden tasks

22 rated tasks · 20 tasks with β ≥ 0.5 (90.9%)

β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.

Source unit: O*NET 27.2 tasks → O*NET 31.0 occupation code
All rated tasks are in the O*NET 31.0 task list.

Source: OpenAI "GPTs are GPTs" exposure rubric
Release: gh-main-0471612
License: MIT License, Copyright (c) 2024 OpenAI

Tasks of O*NET-SOC 2010 occupation 19-4099.02 Precision Agriculture Technicians, linked to this occupation by the O*NET-SOC 2010 to 2019 crosswalk

Task (O*NET 20.1)	Claude.aiRaw / share %
Document and maintain records of precision agriculture information. 19-4099.02	0.003942.9
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems. 19-4099.02	0.002730.0
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. 19-4099.02	0.002527.1

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

Task (O*NET 27.2)	βE1 + 0.5 × E2
Contact equipment manufacturers for technical assistance, as needed. O*NET Task ID 18290	1.0
Document and maintain records of precision agriculture information. O*NET Task ID 18295	1.0
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability. O*NET Task ID 18278	0.5
Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans. O*NET Task ID 18279	0.5
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS). O*NET Task ID 18281	0.5
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. O*NET Task ID 18282	0.5
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials. O*NET Task ID 18284	0.5
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns. O*NET Task ID 18287	0.5
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data. O*NET Task ID 18288	0.5
Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history. O*NET Task ID 18289	0.5
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history. O*NET Task ID 18293	0.5
Draw or read maps, such as soil, contour, or plat maps. O*NET Task ID 18296	0.5
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology. O*NET Task ID 19800	0.5
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. O*NET Task ID 19801	0.5
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems. O*NET Task ID 19802	0.5
Provide advice on the development or application of better boom-spray technology to limit the overapplication of chemicals and to reduce the migration of chemicals beyond the fields being treated. O*NET Task ID 19803	0.5
Use geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics such as nitrogen, phosphorus, or potassium content, pH, or micronutrients. O*NET Task ID 20872	0.5
Analyze data from harvester monitors to develop yield maps. O*NET Task ID 20874	0.5
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions. O*NET Task ID 20875	0.5
Prepare reports in graphical or tabular form, summarizing field productivity or profitability. O*NET Task ID 20876	0.5
Show 10 more tasks ▾Show less ▴

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page

All O*NET 31.0 task statements (22)

All O*NET 31.0 task statements for this occupation (English, as published; task type core or supplemental). Exposure values are not listed here; they are in the table above, next to each task statement as worded in the O*NET version each source used.

Task (O*NET 31.0)	Type
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.	Core
Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.	Core
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.	Core
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).	Core
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.	Core
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.	Core
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.	Core
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.	Core
Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history.	Core
Contact equipment manufacturers for technical assistance, as needed.	Core
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.	Core
Document and maintain records of precision agriculture information.	Core
Draw or read maps, such as soil, contour, or plat maps.	Core
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.	Core
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.	Core
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.	Core
Provide advice on the development or application of better boom-spray technology to limit the overapplication of chemicals and to reduce the migration of chemicals beyond the fields being treated.	Core
Use geospatial technology to develop soil sampling grids or identify sampling sites for testing characteristics such as nitrogen, phosphorus, or potassium content, pH, or micronutrients.	Core
Demonstrate the applications of geospatial technology, such as Global Positioning System (GPS), geographic information systems (GIS), automatic tractor guidance systems, variable rate chemical input applicators, surveying equipment, or computer mapping software.	Core
Analyze data from harvester monitors to develop yield maps.	Core
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.	Core
Prepare reports in graphical or tabular form, summarizing field productivity or profitability.	Core

Anthropic, "What work can robots do?"2026-09

0.35/ 3· Robot exposure index

0 none3 unstructured settings

Scale, basis and source

Each physical task is graded 0–3 by the setting in which robots that have been deployed, sold or demonstrated could do it (0 none · 1 purpose-built robot settings such as a fenced machine cell or a bottling line · 2 structured facilities such as a hospital pharmacy or a container port · 3 unstructured settings such as a private home or a construction site), and the grades are averaged by share of working time

Physical tasks among this occupation's O*NET 29.3 tasks

Anthropic estimated the grades and time shares with Claude Opus 5 using web search, as of 2026.

Source dataset

The robot exposure index is a different measure from the AI exposure figures on this site and cannot be added to or compared with them. It does not measure actual adoption or cost, and 0 also covers occupations with no physical tasks.

Tier of each physical task

Robot environment tiers (E0–E3)

E0 noneDetails
Rated not doable by robots that have been deployed, sold or demonstrated, even in a purpose-built setting
E1 purpose-built robot settingsDetails
Rated doable in a setting built around the machine, such as a fenced machine cell or a bottling line
E2 structured facilitiesDetails
Rated doable in a human work facility that an institution keeps to a working standard, such as a hospital pharmacy or a container port
E3 unstructured settingsDetails
Rated doable in a setting that no institution keeps to a working standard, such as a private home or a construction site

Only tasks classified as physical are listed; other tasks have no tier in the source.

Task (O*NET 29.3)	Robot environment tier
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.	E0 none
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).	E3 unstructured settings
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.	E3 unstructured settings
Demonstrate the applications of geospatial technology, such as Global Positioning System (GPS), geographic information systems (GIS), automatic tractor guidance systems, variable rate chemical input applicators, surveying equipment, or computer mapping software.	E0 none

Employment outlook (BLS)

Values for the whole SOC 19-4012 group (Agricultural technicians), not for this O*NET occupation alone.

Employment, 2025: 20K
Projected employment, 2035: 21K
Change (2025–35): +5.4%
Median annual wage, 2025: $49,630
Typical education needed for entry: Associate's degree

lowest $30,890median wage across 825 detailed occupations, BLS EP 2025–35highest $559,030

Source: U.S. Bureau of Labor Statistics, Employment Projections 2025–35 — Employment by detailed occupation, 2025 and projected 2035

Korea classification links (KSCO)

Links from the current release of the Korea Occupation Direct Crosswalk v1.1, made by AI Changing Work. Relation values are read from the O*NET-SOC occupation side.

KSCO code	KSCO title	Relation	Status
21321	농업 시험원	Related, not a match	confirmed

Relation and status definitions(kr-direct v1.1)

ILO occupational description

AI Changing Work links the ISCO-08 unit groups below to this occupation by applying the U.S. Bureau of Labor Statistics crosswalks (2018 SOC to 2010 SOC, 2010 SOC to ISCO-08) as published, without case-by-case selection; each link may be whole or partial.

ISCO-08 3142Agricultural Technicians

The definition, tasks and examples below are the ILO's description of that unit group, not of this occupation.

Agricultural technicians perform tests and experiments, and provide technical and scientific support to agricultural scientists, farmers and farm managers.

Tasks

(a) preparing materials and equipment for experiments, tests and analyses;
(b) collecting and preparing specimens such as soils, plant or animal cells, tissues or parts or animal organs for experiments, tests and analyses;
(c) assisting with and performing experiments, tests and analyses applying methods and techniques such as microscopy, histochemistry, chromatography, electrophoresis and spectroscopy;
(d) identifying pathogenic micro-organisms and insects, parasites, fungi and weeds harmful to crops and livestock, and assisting in devising methods of control;
(e) analysing produce to set and maintain standards of quality;
(f) conducting or supervising operational programmes such as fish hatchery, greenhouse and livestock production programmes;
(g) analysing samples of seeds for quality, purity and germination rating;
(h) collecting data and estimating quantities and costs of materials and labour required for projects;
(i) organizing maintenance and repairs of research equipment.

Example occupations

Dairy Technician · Field Crop Technician · Herd Tester · Horticultural Technician · Poultry Technician

Sources: ILO ISCO-08 — details: Credits

ESCO occupational vocabulary

Each entry below is matched to this occupation in the published ESCO-O*NET Crosswalk (version 1, 2022). The match types are that crosswalk's; the wording is AI Changing Work's.

agricultural engineerESCO 2144.1.2Overlapping, not the same
Agricultural engineers intervene in a variety of matters within the agriculture field in combination with engineering concepts. They design and develop machinery and equipment for an efficient and sustainable exploitation of the land. They advise on the use of resources in agricultural sites comprising the usage of water and soil, harvesting methods, and waste management.
agricultural scientistESCO 2132.1Overlapping, not the same
Agricultural scientists research and study soil, animals and plants with the objective of improving agricultural processes, the quality of agricultural products or the impact of agricultural processes on the environment. They plan and implement projects such as development projects on behalf of clients or institutions.

Source: European Commission, DG EMPL — ESCO API 2026-08-08 (bridge v1.2.1)

License: CC BY 4.0 (EU Reuse Notice)

View original

ESCO reuse terms and the modification notice are in Credits.

France classification links (ROME)

AI Changing Work's links between this occupation and ROME 4.0 job profiles, judged with AI models. Relations are read from this occupation's side. Links marked “provisional” were adopted on the judgments of two judges from the same model family (Claude) alone, or by an operator rule applied to their judgments, so they rest on weaker evidence than confirmed links.

ROME code	ROME title (French)	Relation
A1301	Technicien / Technicienne en agriculture	Related, not a match
A1304	Conseiller / Conseillère technique agricole	Related, not a match
M1895	Géomaticien / Géomaticienne	Related, not a match

Source: France Travail — Répertoire Opérationnel des Métiers et des Emplois (ROME 4.0), version 61, last updated 15 June 2026 (https://www.data.gouv.fr/datasets/repertoire-operationnel-des-metiers-et-des-emplois-rome). Licence Ouverte 2.0.

ROME codes and titles are reproduced unchanged; the links to O*NET-SOC occupations are AI Changing Work's own judgment and are not a France Travail correspondence.

(AI Changing Work link release: fr-direct v1)

Spain classification links (CNO-11)

AI Changing Work's links between this occupation and CNO-11 occupations, judged with AI models. Relations are read from this occupation's side.

CNO-11 code	CNO-11 title (Spanish)	Relation
2424	Ingenieros técnicos agrícolas	Related, not a match
3142	Técnicos agropecuarios	Related, not a match

Source: Instituto Nacional de Estadística (INE), National Classification of Occupations 2011 (CNO-11, Royal Decree 1591/2010) — www.ine.es · Licensed CC BY 4.0. CNO-11 codes and Spanish titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not an INE correspondence.

(AI Changing Work link release: es-direct v1)

Germany classification links (KldB)

AI Changing Work's links between this occupation and KldB occupations, judged with AI models. Relations are read from this occupation's side.

KldB code	KldB title (German)	Relation
11113	Berufe in der Landtechnik - komplexe Spezialistentätigkeiten	Related, not a match
11183	Berufe in der Landwirtschaft (sonstige spezifische Tätigkeitsangabe) - komplexe Spezialistentätigkeiten	Related, not a matchprovisional

Source: © Statistik der Bundesagentur für Arbeit — Klassifikation der Berufe 2010 – überarbeitete Fassung 2020, https://statistik.arbeitsagentur.de. KldB codes and titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own mapping (eigene Zuordnung) and are not a Bundesagentur für Arbeit correspondence.

(AI Changing Work link release: de-direct v1)

Japan classification links (JSCO)

AI Changing Work's links between this occupation and JSCO occupations, judged with AI models. Relations are read from this occupation's side.

JSCO code	JSCO title (Japanese)	Relation
061	農林水産技術者	This occupation is narrower

Source: Ministry of Internal Affairs and Communications (MIC), Japan Standard Occupational Classification (Dec. 2009, MIC Notification No. 555). JSCO codes and Japanese titles are reproduced unchanged; the links to O*NET occupations are AI Changing Work's own judgment and are not a MIC correspondence.

(AI Changing Work link release: jp-direct v1)

Sources, licenses and AI Changing Work's modification and translation notices for this page: Credits & sources