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AI Automation Index
Will AI Replace Precision Agriculture Technicians?
Task-level automation exposure for Precision Agriculture Technicians: 22 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
59%
weighted task index
22 of 22 tasks scored
Median Salary
$49,630 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
15,130 people hold this job — size, not growth
Projected 2025–35
+5.4%
employment change
2,800 openings a year · BLS line item
Pay by state
BLS OEWS publishes 39 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$37,840 – $64,070
Highest medians
Iowa $64,070
Wyoming $63,120
Arizona $62,990
California $60,320
Kentucky $59,860
Lowest medians
Oklahoma $37,840
Virginia $41,210
South Carolina $41,290
South Dakota $41,420
New Hampshire $42,520
State figures come from the 19-4012 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 59%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 14%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 60%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 10%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 9/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
22 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 70%
15 tasks
Medium exposure (≥ 35%) 16%
4 tasks
Low exposure (below 35%) 14%
3 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 05/2025 · AI/SME
Operate drone technology to capture aerial imagery and data for crop monitoring and analysis.
Constituent Tasks
#18295 · Core · 82% automatable
Document and maintain records of precision agriculture information.
data analysis — Record research or operational data into schemas
Record research or operational data.
#18281 · Core · 10% automatable
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).
situated mobility — Field data collection requires being in the field
Collect geographical or geological field data.
#20872 · Core · 72% automatable
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.
data analysis — Statistical analysis of environmental datasets is automatable
Analyze environmental data.
#18284 · Core · 78% automatable
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.
data analysis — Computes coordinates from data deterministically
Determine geographic coordinates. Analyze geological or geographical data.
#18280 · Core · 18% automatable
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.
physical manipulation — Physical calibration of instruments requires hands
Calibrate scientific or technical equipment. Maintain laboratory or technical equipment.
#18289 · Core · 60% automatable
Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history.
visual inspection — Generates maps from data; cartographic judgment human
Prepare maps.
#18279 · Core · 62% automatable
Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.
information retrieval — Crop method research aided; field trials human
Research crop management methods.
#20875 · Core · 70% automatable
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.
data analysis — Geological/geographic data analysis with human review
Analyze geological or geographical data.
#18282 · Core · 72% automatable
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.
data analysis — Statistical analysis of environmental datasets is automatable
Analyze environmental data.
#20874 · Core · 70% automatable
Analyze data from harvester monitors to develop yield maps.
data analysis — Geological/geographic data analysis with human review
Analyze geological or geographical data.
#19801 · Core · 50% automatable
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.
decision support — Synthesizes research; field application and validation human
Apply knowledge or research findings to address environmental problems.
#20873 · Core · 70% automatable
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.
data analysis — Geological/geographic data analysis with human review
Analyze geological or geographical data.
#18296 · Core · 60% automatable
Draw or read maps, such as soil, contour, or plat maps.
visual inspection — Generates maps from data; cartographic judgment human
Prepare maps.
#18288 · Core · 70% automatable
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.
information retrieval — Product-use advice is largely informational with review
Advise customers on the use of products or services.
#20876 · Core · 80% automatable
Prepare reports in graphical or tabular form, summarizing field productivity or profitability.
text generation — Generate operational reports from structured data
Prepare operational reports.
#19803 · Core · 65% automatable
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.
decision support — New-tech advice needs organizational context judgment
Advise others on the development or use of new technologies.
#18278 · Core · 18% automatable
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.
physical manipulation — Physical calibration of instruments requires hands
Calibrate scientific or technical equipment.
#19802 · Core · 45% automatable
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.
decision support — AI proposes agricultural methods; field validation required
Develop agricultural methods.
#18293 · Core · 58% automatable
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.
data analysis — Climate data analysis aided; instrument networks human
Conduct climatological research.
#19800 · Core · 65% automatable
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.
decision support — New-tech advice needs organizational context judgment
Advise others on the development or use of new technologies.
#18290 · Core · 56% automatable
Contact equipment manufacturers for technical assistance, as needed.
interpersonal — Discussing design features with staff is relational exchange
Discuss design or technical features of products or services with technical personnel. Consult with others regarding safety or health equipment or facilities.
#18287 · Core · 72% automatable
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.
data analysis — Statistical analysis of environmental datasets is automatable
Analyze environmental data.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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