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# Will precision agriculture technicians be replaced by AI?

Updated October 2026

Precision agriculture technicians score 50 out of 100 for AI exposure. AI helps with 71% of their task time, and they stay in charge. AI can do another 19%. 10% needs a person.

-   **19%** AI can do it
-   **71%** AI helps
-   **10%** Needs a person

High exposure

Higher than 75% of the 923 jobs we scored.

## Task by task

We graded 22 tasks, most exposed first. AI can do 4 of them, helps with 16 and leaves 2 to people.

| Task | AI exposure |
| --- | --- |
| Prepare reports in graphical or tabular form, summarizing field productivity or profitability. Summary reports and tables from field data are highly automatable. | 75% AI can do it |
| Document and maintain records of precision agriculture information. Record keeping is mostly digital data entry and organization. | 70% AI can do it |
| 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. Sampling grid generation is handled by GIS software tools. | 65% AI can do it |
| Analyze data from harvester monitors to develop yield maps. Harvester monitor data cleaning and yield mapping is software-driven. | 65% AI can do it |
| Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history. GIS copilots speed map layering; technician verifies outputs. | 62% AI helps |

## How precision agriculture technicians compare

All jobs (923)

Life, physical and social science (60)

The dark line is **precision agriculture technicians, at 50**. Each dot is a score, and darker dots mean more jobs share it. The short marks are the averages, 37 for all jobs and 47 for life, physical and social science.

## Pay and outlook in the US

Median pay

$49,630 a year, May 2025

Projected change in jobs

+5.4% from 2024 to 2034

## Skills to build

-   Calibrate GPS guidance and variable-rate sensors on equipment in the field
-   Run live demonstrations of GPS guidance for growers on site
-   Walk fields to check AI-drafted yield maps against soil results
-   Build management zones in ESRI ArcGIS software with agronomists
-   Advise growers on GPS receiver and satellite correction upgrades
-   Interpret boom-spray drift results with growers and agronomists

## Safer roles nearby

5 related jobs with lower scores.

-   [Soil scientists41 out of 100](https://www.aigentcy.com/ai-exposure/soil-scientists/)
-   [Agricultural engineers34 out of 100](https://www.aigentcy.com/ai-exposure/agricultural-engineers/)
-   [Agricultural technicians32 out of 100](https://www.aigentcy.com/ai-exposure/agricultural-technicians/)
-   [Conservation scientists43 out of 100](https://www.aigentcy.com/ai-exposure/conservation-scientists/)
-   [Geological technicians43 out of 100](https://www.aigentcy.com/ai-exposure/geological-technicians/)

## Questions about precision agriculture technicians and AI

Will AI replace precision agriculture technicians?

Partly. About 5 in 10 working hours go to tasks AI can already do, mostly summary reports and record keeping. Installing sensors, calibrating GPS guidance, and running in-person demonstrations stay with technicians because they need a person physically working on equipment in the field.

Which tasks is AI taking over for precision agriculture technicians?

Report preparation scores 75 on exposure, the highest in this job, so summary tables of field productivity and profitability can be generated from field data. Record keeping scores 70 and is mostly digital data entry and organization. Yield mapping from harvester monitor data scores 65 and is software-driven.

Which tasks stay with precision agriculture technicians?

Installing and calibrating sensors and GPS guidance systems scores 12 on exposure, the lowest in this job, because it needs hands-on work with physical hardware. Live equipment demonstrations score 18 since growers learn by watching a person run the machine. Field data collection scores 30 because someone must be in the field to collect it.

How can precision agriculture technicians stay valuable?

Take responsibility for sensor calibration and GPS guidance work on the equipment, since that scores lowest for exposure. Check every AI-drafted management zone and yield map in ESRI ArcGIS software against field walks and soil results. Build advice skills on boom-spray drift and GPS upgrades so growers pay for judgment, and keep Excel and Access records audit ready.

## Related jobs

-   [Industrial ecologists52 out of 100](https://www.aigentcy.com/ai-exposure/industrial-ecologists/)
-   [Forest technicians32 out of 100](https://www.aigentcy.com/ai-exposure/forest-technicians/)
-   [Farm managers36 out of 100](https://www.aigentcy.com/ai-exposure/farm-managers/)
-   [Range managers39 out of 100](https://www.aigentcy.com/ai-exposure/range-managers/)

## Next steps for precision agriculture technicians

-   ### Check your job
    
    Your job title is a rough guide. Your tasks decide your score. Paste your job description or CV to see which tasks AI can do.
    
    [Check your job](https://www.aigentcy.com/tools/ai-exposure-checker/) [Check your CV](https://www.aigentcy.com/tools/cv-checker/)
    
-   ### Estimate team savings
    
    The score is per person. Add headcount and salary to see the hours AI can take on in a team of precision agriculture technicians, and what they cost.
    
    [Estimate what AI saves a team of precision agriculture technicians](https://www.aigentcy.com/tools/team-ai-audit/?role=precision-agriculture-technicians) [Work out the return on automation](https://www.aigentcy.com/tools/ai-savings-calculator/)
    

### Check which roles in your team AI can take on

We map your team's tasks, show what AI can do today and build the automations that pay back first.

[Book an AI audit](https://www.aigentcy.com/audit/)

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Sources and licences

Occupation data from the O\*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) and modified by Aigentcy. O\*NET® is a trademark of USDOL/ETA. Wage, employment and projection data from the U.S. Bureau of Labor Statistics. EU occupation names from ESCO, European Commission. AI usage signals from the Anthropic Economic Index (CC BY) and Microsoft Research (CC BY 4.0). [Methodology and sources](https://www.aigentcy.com/ai-exposure/methodology/). Scores © Aigentcy. Quote them with a link to the page.
