Precision agriculture mapping with drones
Learn how drone photogrammetry and crop imaging produce maps that support runoff planning and variable-rate spraying.
Precision agriculture mapping: why drones are used
A drone map turns field imagery into a layer that can guide planting, crop care and input use. The value lies in what the operator can do with that layer, not in the image alone.
- XAG P150 30 L/min
- DJI Agras T100 30 L/min
- DJI Agras T25P 24 L/min
- XAG P100 Pro 22 L/min
| Model | Value |
|---|---|
| XAG P150 | 30 L/min |
| DJI Agras T100 | 30 L/min |
| DJI Agras T25P | 24 L/min |
| XAG P100 Pro | 22 L/min |
Sources: xa.com, ag.dji.com, ag.dji.com, xa.com
Photogrammetry builds spatial data from overlapping photographs. Structure from Motion photogrammetry uses images taken from different angles and positions to create a three-dimensional surface.
Researchers led by Penn State used this method to map hydrologically sensitive areas. These are places where water tends to collect or flow, raising runoff risk. They also mapped phosphorus critical source areas, where phosphorus may wash into streams.
Those results show why resolution and accuracy matter. A coarse field image may show broad crop patterns. A precise surface model can also reveal terrain that affects runoff and field planning.
This level of detail can help farmers identify places to avoid during planting. It can also support plans for riparian buffers and other runoff controls. Drone flights make it possible to refresh the map after the land changes, rather than waiting for another LiDAR survey.
Crop mapping has a related role. Drone imagery can help monitor crop health, find planting flaws, and detect diseases and pests. Connected farm systems then bring those findings into decisions about crop inputs.
One farmer cited in the research used drone mapping to determine the best use of phytosanitary products. His aim was to “retrieve a maximum amount of information” from the field plots. That sums up the practical case for mapping: collect useful detail, then link it to a field action.
From aerial capture to mapping outputs
Field mapping and spray planning have often required long hours on the ground. Even early drone workflows could need a separate mapping aircraft, software and computer. Network access could also hold up image transfer and map building in remote areas.
Newer workflows seek to join capture, map building and route planning more closely. DJI has described an agricultural drone workflow that combines aerial crop protection with local mapping. Its cited system can create maps without an internet connection and turn captured images into orthophotos for field work.
The main outputs depend on the job. They can include:
- an orthophoto for field boundaries and visible features;
- a three-dimensional terrain surface made with photogrammetry;
- a crop-health map based on RGB or multispectral imagery;
- runoff and phosphorus-risk areas;
- obstacles and no-spray zones;
- a prescription map for variable-rate work.
An orthophoto gives the operator a corrected overhead map rather than a single angled image. A terrain model adds height and surface form. Crop-health layers show variation within the crop that may need closer checks or a change in treatment.
Multispectral imaging records selected parts of the light spectrum. The DJI Mavic 3M covers Green at 560±16 nm, Red at 650±16 nm, Red Edge at 730±16 nm and NIR at 860±26 nm. Its multispectral cameras have 5 MP resolution and a maximum image size of 2592×1944.
The same aircraft has a 20 MP RGB camera with a maximum image size of 5280×3956. Its RTK positioning accuracy is 1 cm + 1 ppm horizontally and 1.5 cm + 1 ppm vertically. Maximum flight time is 43 min without wind.
A map still needs to match the agronomic question. Terrain data suits runoff work, while multispectral or RGB crop imagery supports crop-health assessment. The output should be chosen before capture so the flight gathers the right source data.
High resolution alone does not make an agronomic decision. It provides more detail for that decision. The field team still needs to link the mapped pattern with the crop, terrain or input issue being managed.
Turning maps into variable-rate prescriptions
A crop-health map shows variation. A prescription map tells the application system how to respond to that variation.
XAG and PIX4D released a prescription-map workflow for crop protection, fertiliser spreading and seeding. Users create the maps in PIX4Dfields, then export them in a format that XAG agricultural drones can interpret.
The cited example shows the XAG P100 Pro using a prescription map for variable-rate spraying. The drone changes the application rate in flight according to crop-health data. It can apply pesticides, fertilisers or growth regulator to defined parts of the field.
The map can also affect the route. The drone follows a pre-set path, targets zones that need treatment and skips areas that do not. Field boundaries, obstacles and no-spray zones can shape the planned route.
This is the key link between mapping and variable-rate application. The workflow moves through distinct tasks:
- mark zones that need different treatment;
- assign an input rate to each zone;
- export the prescription in a compatible format;
- load it into the application workflow;
- fly the set route and change rates in the field.
The XAG One App also lets users draw prescription maps by assigning different doses to target zones. PIX4Dfields adds another route from aerial crop analysis to the application drone.
Another drone-derived mapping system cited in the research produces prescription maps for fertilisers, fungicides and growth regulators. It uses RGB or multispectral imagery and can support field work when cloud blocks satellite images. This shows the wider mapping-to-operations pattern: collect current imagery, analyse it and send the result to the applicator.
Using drone mapping to streamline field work
The strongest workflow starts with the field decision, not the flight. An operator should know whether the task concerns runoff, crop stress, planting flaws, pests or input rates. That choice sets the needed image type and map output.
Flight planning then needs to cover the field with enough overlapping imagery for photogrammetry. If position accuracy is central to the job, the operator should also check the aircraft’s RTK or PPK specification. Ground checks remain useful when the map will guide a costly or sensitive action.
After capture, software can stitch the images into an orthophoto or surface model. Crop imagery can then be analysed for patterns that need action. The team can turn those zones into a prescription map rather than treating the whole field at one rate.
The last step is field delivery. A useful map must reach the drone or other machine in a form it can read. The operator should confirm file support, field boundaries, obstacles, no-spray zones and assigned rates before starting work.
This check matters because map production and product application are different functions. A mapping aircraft may produce detailed imagery but carry no spray load. An application drone may accept a prescription map, but that does not prove it can create every crop-health layer needed to make one.
For more background on choosing and using this equipment, see the Guides index. Operators comparing payload, positioning and flight data can also use the Drone specifications section.
Drone platform considerations for mapping workflows
A precision agriculture mapping system may use one aircraft for capture and another for treatment. It may also use an agricultural drone with mapping tied into its own route-planning system. The right structure depends on the map output and the planned field action.
Mapping coverage and image data
The Wingtra WingtraOne GEN II is specified for up to 59 min of flight time.
It is a mapping platform rather than a liquid or granular applicator.
The DJI Mavic 3M takes a crop-imaging approach with both RGB and multispectral cameras. That mix supports visible field mapping and multispectral crop data from the same aircraft. Its position and image specifications should be checked against the resolution and accuracy needed for the job.
Applying a prescription in the field
The cited PIX4Dfields workflow is the link between the prescription map and variable-rate work by the XAG P100 Pro.
Other application platforms show why map compatibility and application capacity should be checked as separate items.
The DJI Agras T25P has a 30 L spreading tank. Its maximum flow rate is 24 L/min with four nozzles and 16 L/min with two. Both the DJI Agras T100 and DJI Agras T25P have stated RTK hovering accuracy of ±10 cm horizontally and ±10 cm vertically.
These specifications help size an application aircraft, but they do not by themselves confirm prescription-file support. Operators should verify the whole data chain before choosing a platform: imagery, map building, prescription export, route planning and in-flight rate control.
Maps should end in a clear field action
Drone mapping earns its place when it changes a plan. A runoff map can mark land to avoid during planting. A crop-health layer can define zones for closer checks. A prescription map can then set where an application drone flies and how its rate changes.
That chain turns aerial data into field work. It also keeps the process testable: the source image, mapped zone, assigned rate and flown route can each be checked.
The aircraft is only one part of the system. Image type, position accuracy, software format and application control all need to fit together. When they do, drone mapping becomes more than a field picture. It becomes the working map behind a variable-rate action.