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.
U.S. Workers
71,400
Median Salary
$60,130
10-Year Growth
+3.5%
Annual Openings
10,600
Typical entry: Associate's degree
22 of 22 tasks have some AI capability
Exposure Trend
This score reflects estimated AI technical capability for tasks in this occupation. It does not predict employment changes, and it does not account for company-specific constraints, regulation, or adoption barriers.
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.
AI: Fully automatable - AI and GIS tools can fully compute optimal soil-sampling grids and identify sampling sites from spatial and agronomic data, automating the planning step end-to-end.
Document and maintain records of precision agriculture information.
AI: Fully automatable - Automated systems and AI can ingest, validate, organize, and maintain precision-agriculture records reliably with minimal human oversight as of 2025.
Identify spatial coordinates, using remote sensing and Global Positioning System (GPS) data.
AI: Fully automatable - AI and geospatial toolchains already reliably extract and convert remote sensing and GPS data into spatial coordinates at operational accuracy.
Create, layer, and analyze maps showing precision agricultural data, such as crop yields, soil characteristics, input applications, terrain, drainage patterns, or field management history.
AI: Fully automatable - Mapping, layering, and quantitative analysis of precision-agriculture datasets is well within current GIS/AI capabilities and can be fully automated.
Analyze data from harvester monitors to develop yield maps.
AI: Fully automatable - Processing harvester monitor logs and producing yield maps is a standardized data-processing task that AI tools already perform reliably.
Analyze geospatial data to determine agricultural implications of factors such as soil quality, terrain, field productivity, fertilizers, or weather conditions.
AI: Fully automatable - AI and geospatial analytics can integrate soil, terrain, productivity, fertilizer, and weather data to determine agricultural implications and produce actionable analyses.
Contact equipment manufacturers for technical assistance, as needed.
AI: Fully automatable - Automated systems and agents can reliably contact manufacturers via email/ticketing/chat and manage routine technical assistance interactions.
Draw or read maps, such as soil, contour, or plat maps.
AI: Fully automatable - Reading and producing soil, contour, and plat maps is a mature capability of GIS and AI tools and can be fully automated for standard use cases.
Prepare reports in graphical or tabular form, summarizing field productivity or profitability.
AI: Fully automatable - By 2025 AI systems can readily ingest yield and financial datasets and generate graphical/tabular reports and summaries automatically with minimal human intervention.
Divide agricultural fields into georeferenced zones, based on soil characteristics and production potentials.
AI: Fully automatable - Automated geospatial clustering and zoning based on soil and productivity data is a well-established, automatable task using modern GIS and ML tools.
Advise farmers on upgrading Global Positioning System (GPS) equipment to take advantage of newly installed advanced satellite technology.
AI: Fully automatable - Advising on GPS upgrades and compatibility with new satellite services is primarily an information and configuration task that AI can fully perform given equipment and service data.
Collect information about soil or field attributes, yield data, or field boundaries, using field data recorders and basic geographic information systems (GIS).
AI: Partial - AI coupled with autonomous sensors and drones can collect many soil and field measurements and integrate them into GIS, but manual sampling, equipment setup, and edge-case troubleshooting still need technicians.
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.
AI: Partial - AI can produce interactive tutorials, simulations, and step-by-step guidance demonstrating geospatial applications, but live hands-on demonstrations and situational troubleshooting are not yet fully automatable.
Apply precision agriculture information to specifically reduce the negative environmental impacts of farming practices.
AI: Partial - AI can generate recommendations to reduce environmental impacts, but implementation, regulatory judgment, and on‑farm adaptation still require significant human oversight.
Install, calibrate, or maintain sensors, mechanical controls, GPS-based vehicle guidance systems, or computer settings.
AI: Partial - Physical installation, calibration, and mechanical maintenance require skilled hands and site-specific judgment; AI can guide and assist but not fully replace technicians in most cases.
Program farm equipment, such as variable-rate planting equipment or pesticide sprayers, based on input from crop scouting and analysis of field condition variability.
AI: Partial - AI can generate variable-rate prescriptions and configuration files, but safe programming and device-specific deployment typically require human verification and on-site setup.
Compare crop yield maps with maps of soil test data, chemical application patterns, or other information to develop site-specific crop management plans.
AI: Partial - AI can align and analyze geospatial layers to suggest site-specific management options, but full plan development typically requires human agronomic judgment and local validation.
Recommend best crop varieties or seeding rates for specific field areas, based on analysis of geospatial data.
AI: Partial - AI can recommend varieties and seeding rates from geospatial and historical data, but recommendations often need human oversight for local disease, market and operational constraints.
Analyze remote sensing imagery to identify relationships between soil quality, crop canopy densities, light reflectance, and weather history.
AI: Partial - AI can detect correlations between imagery, canopy indices and weather history, but accurately inferring soil quality and causal relationships from remote sensing remains partially uncertain and requires ground truthing.
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.
AI: Partial - AI can provide evidence-based design and operational recommendations for boom-spray technology, but engineering development, field testing, and regulatory considerations limit full automation.
Identify areas in need of pesticide treatment by analyzing geospatial data to determine insect movement and damage patterns.
AI: Partial - AI can analyze geospatial and sensor data to flag likely pest hotspots and damage patterns, yet precise identification of insect movement and treatment decisions still need validation and human oversight.
Participate in efforts to advance precision agriculture technology, such as developing advanced weed identification or automated spot spraying systems.
AI: Partial - AI can substantially contribute to R&D (model training, image annotation, control algorithms) for weed ID and spot spraying, but full participation in multidisciplinary hardware development and field trials requires human teams.