Agricultural drone sensors explained
Learn how flight, terrain, obstacle, and imaging sensors work together for precision spraying and mapping—then match sensors to your field tasks.
What “agricultural drone sensors” mean in practice
Those parts do different jobs. Treating them as one system can hide what the drone can measure and how it controls field work.
- DJI Agras T100 100 L
- DJI Agras T70P 70 L
- XAG P150 70 L
- DJI Agras T55 50 L
- DJI Agras T50 40 L
- Yamaha FAZER R 32 L
| Model | Value |
|---|---|
| DJI Agras T100 | 100 L |
| DJI Agras T70P | 70 L |
| XAG P150 | 70 L |
| DJI Agras T55 | 50 L |
| DJI Agras T50 | 40 L |
| Yamaha FAZER R | 32 L |
Sources: ag.dji.com, ag.dji.com, xa.com, ag.dji.com, ag.dji.com, global.yamaha-motor.com
Flight sensors help the aircraft hold its position, follow terrain and detect hazards. Radar, vision and LiDAR can support terrain following and obstacle awareness.
Task sensors collect crop or field data. Thermal sensors record infrared imagery.
The operator can set flight and application parameters before the job.
These tools support two linked aims. The drone gathers data about the field, then that data can guide a map, flight or treatment plan. This can improve targeting and reduce blanket chemical use.
The key point is simple: the platform carries and powers the system, but the sensors define what it can see. The spray or spread hardware defines what it can apply. Operators comparing aircraft can start with the drone specifications, then check whether the sensor and work flow fit the job.
Sensor use case 1: Precision application and why accuracy matters
Agricultural drones can apply fertilisers, pesticides and herbicides to set parts of a field. A pre-programmed flight can direct spot spraying towards areas that need treatment. This differs from blanket coverage across the whole field.
Better targeting can cut chemical waste and cost. It can also reduce over-application and runoff into nearby land or water. The benefit depends on a sound treatment map, precise flight control and the correct output settings.
Position sensing keeps the route on line
RTK is part of the flight-control side of precision application. It helps the drone hold its planned position rather than measure crop condition. Several application platforms in the supplied product data state an RTK hovering accuracy of ±10 cm horizontally and ±10 cm vertically.
Those figures apply to DJI Agras T100, DJI Agras T25, DJI Agras T25P, DJI Agras T50, DJI Agras T55 and DJI Agras T70P. They do not show spray quality on their own. Nozzle set-up, flow, route planning and the treatment plan remain separate parts of the job.
Terrain and obstacle sensing affect application height
Application drones often work close to crops. Changes in ground or canopy height can alter the distance between the spray system and the target. Radar and vision sensors can help a drone adjust its flight height over difficult terrain.
LiDAR offers another form of situational awareness. It emits laser light and measures the return from vegetation, ground or obstacles. The resulting high-resolution 3-D field map can help with terrain following and the detection of wires, poles and trees.
Obstacle range is a useful platform figure, but it is not a promise that every hazard will be found. DJI Agras T25 and DJI Agras T50 each list an obstacle-sensing range of 1–50 m, with a 2.5 m safety limit distance. DJI Agras T100, DJI Agras T25P and DJI Agras T70P state a safety-system range of no more than 60 m, while DJI Agras T55 states 60 m.
Output hardware must match the planned rate
Sensor-led targeting has little value if the aircraft cannot deliver the planned output. Tank size and maximum flow rate therefore belong in the same buying check as positioning and obstacle sensing.
DJI Agras T25 and DJI Agras T25P each carry a 20 L spray tank. Both reach 16 L/min with two outlets and 24 L/min with four. DJI Agras T50 raises spray capacity to 40 L while using the same stated maximum flow figures.
Larger platforms offer more liquid capacity. DJI Agras T70P carries 70 L and reaches 30 L/min, or 40 L/min with the optional four-nozzle set-up. DJI Agras T100 carries 100 L and has the same stated flow rates.
Other application-led options use different layouts. XAG P100 Pro has a 50 kg payload capacity, an 80 L container and a 50 L smart tank. Its maximum flow rate is 22 L/min. XAG P150 has a 70 L smart liquid tank and a maximum flow rate of 30 L/min.
These figures describe capacity and peak output. They do not state that a platform will suit every crop, chemical or rate. The treatment plan still needs to fit the field and the chosen application system.
Sensor use case 2: Mapping and field understanding
Mapping starts with repeatable image or distance measurements. The operator flies a planned route, and the sensor records overlapping or spatially referenced field data. The output may support crop monitoring, terrain models, boundaries, obstacles or later treatment work.
It can support high-definition field images and crop inspection. Research on fruit farms has also used aerial images and spectral data to study tree height, canopy volume, health, water, nutrients and possible yield.
Multispectral and thermal data add measurements that the eye cannot make from a normal colour image. Thermal sensing can help identify dry parts of a field. Multispectral sensing can support plant counting, spectral analysis and season-long canopy work.
A multispectral platform example
DJI Mavic 3M combines an RGB camera with multispectral imaging. Its multispectral bands are Green 560±16 nm, Red 650±16 nm, Red Edge 730±16 nm and NIR 860±26 nm.
The multispectral camera has a resolution of 5 MP and a maximum image size of 2592×1944. The RGB camera has a resolution of 20 MP and a maximum image size of 5280×3956. This makes the aircraft a clear example of a platform where the measurement payload, rather than a spray tank, defines the farm task.
DJI Mavic 3M states RTK positioning accuracy of 1 cm + 1 ppm horizontally and 1.5 cm + 1 ppm vertically. Its maximum flight time is 43 min without wind. Position accuracy and flight time both shape how an operator plans repeat surveys.
Mapping is also a navigation tool
A map does not only serve later crop analysis. A high-resolution 3-D map can help the aircraft understand terrain and hazards during flight. That matters around uneven canopies, slopes, poles, trees and wires.
Digital elevation models can also guide field work, planting direction, water management and soil conservation plans. Multispectral or thermal data may show areas that are dry or need closer checks. Such outputs can also feed variable-rate prescription maps for pest, disease and weed treatment.
The survey platform affects how much ground can be recorded. Wingtra WingtraOne GEN II covers 460 ha per flight at 120 m altitude and 2.7 cm/px GSD. It has an absolute accuracy of 3 cm RMS across x, y and z with RTK/PPK, and a maximum flight time of up to 59 min.
Its payload capacity is 800 g and its maximum take-off weight is 4.8 kg. Those figures help define the survey envelope. They do not confirm that any unlisted sensor will fit or work with the aircraft.
Sensor use case 3: Plant and field monitoring with spectral imaging
Crop monitoring needs a different sensor choice from spraying. A spray drone must put material on a target. A monitoring drone must measure something that answers the grower’s question.
RGB is useful when visible detail is the main need. Multispectral cameras split reflected light into selected bands, allowing analysis beyond a standard colour image. Research links high-resolution spectral data with plant growth, health, water status, nutrient status and biomass estimates.
Thermal sensors add dynamic infrared imagery. They can support field research and analysis where heat patterns matter. Thermal and multispectral sensing may also help identify areas that need closer inspection, rather than replacing an agronomic check.
Dedicated agricultural sensors show why platform and payload should be assessed apart. Sensor systems can combine thermal, RGB and precision-filtered multispectral imagers. Other systems pair high-resolution colour images with synchronised multispectral data for plant counting and canopy analysis.
This distinction also protects against a common buying error. A drone may have strong flight performance but no sensor suited to the measurement goal. Another may carry the right camera but lack the range, accuracy or work flow needed for the field.
How to choose agricultural drone sensors: match sensor to task
Start with the field decision, not the aircraft. Write down what the data or treatment must achieve. Then work back to the sensor, flight plan and platform.
A practical short list is:
- For precision application: check route accuracy, terrain response, obstacle sensing, tank size and output range.
- For visible mapping: check RGB image detail, positioning, flight time and area coverage.
- For crop measurement: check whether RGB, multispectral or thermal data suits the crop question.
- For terrain awareness: check the stated role and range of radar, vision or LiDAR sensing.
- For repeat surveys: check positioning accuracy and whether the same route and data process can be used again.
Keep the farm work flow in view. Data must move from capture to a map, check or treatment plan. A high-spec sensor has little value if its output cannot guide a field decision.
Also separate verified specifications from assumed compatibility. Payload capacity does not prove that a given camera will connect to the aircraft. An obstacle-sensing range does not prove detection of every wire or branch. A maximum flight time does not state what the aircraft will achieve in wind.
For broader buying checks, the guides index provides a useful route into related farm-drone topics.
Platform examples in sensor-led agricultural work flows
Application-led platforms put spray or spread hardware at the centre of the system. DJI Agras T25, DJI Agras T25P, DJI Agras T50, DJI Agras T55, DJI Agras T70P and DJI Agras T100 all combine stated application capacities with RTK hovering figures. XAG P100 Pro and XAG P150 also pair liquid or granule containers with stated output rates.
Hylio Ares carries 13 gal (50 L) for spraying and 20 gal (76 L) for spreading. Its spray and spreader configurations have a swath width of up to 40 ft. Yamaha FAZER R takes a different form: it is a petrol-powered unmanned helicopter with a 32 L spray tank.
Survey-led platforms put image capture, position and coverage first. DJI Mavic 3M provides stated RGB and multispectral specifications. Wingtra WingtraOne GEN II provides stated flight, coverage, payload and RTK/PPK accuracy figures.
No single sensor stack covers every farm task. Select the measurement first, then the aircraft that can carry out the flight or application work. That keeps the buying case tied to a result that can be checked.