Will precision agriculture technicians be replaced by AI?
Updated
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 |
| Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials. Clustering tools draft zones; agronomist judgment still needed. | 60% AI helps |
| Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history. Imagery analysis is AI strength; relationships need expert validation. | 58% AI helps |
| Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions. AI can summarize geospatial factors; interpretation needs expert review. | 52% AI helps |
| Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans. Comparing yield and soil layers; AI speeds analysis, planning stays human. | 50% AI helps |
| Draw or read maps, such as soil, contour, or plat maps. Vision tools can read scanned maps; drawing and checking still manual. | 50% AI helps |
Show the other 12 tasks
| Task | AI exposure |
|---|---|
| Contact equipment manufacturers for technical assistance, as needed. AI can draft support requests and troubleshoot; contact is human. | 50% AI helps |
| Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns. Pest movement analysis from geodata; scouting confirmation is physical. | 50% AI helps |
| Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data. GPS hardware provides coordinates; software assists extraction and checks. | 45% AI helps |
| Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data. Seeding rate suggestions from data; agronomic judgment required. | 45% AI helps |
| Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability. Prescription files can be generated; equipment setup needs a person. | 45% AI helps |
| Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices. Recommendations for lower environmental impact need field context. | 40% AI helps |
| 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. Spray technology advice draws on specialized technical knowledge. | 35% AI helps |
| Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems. Research participation; AI assists models but field trials are physical. | 35% AI helps |
| Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS). Field collection needs presence in fields; AI helps process data. | 30% AI helps |
| Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology. Upgrade advice is consultative; AI helps with product comparisons. | 30% AI helps |
| 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. Live hands-on demonstrations of equipment to people in person. | 18% Needs a person |
| Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings. Installing and calibrating physical sensors and hardware on equipment. | 12% Needs a person |
How precision agriculture technicians compare
All jobs (923)
Life, physical and social science (60)
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.
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.
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