Aerial Multispectral Data: The Foundation of Variable Rate Technology in Precision Agriculture

Precision agriculture has always sought to apply the right amount of input at the right place. This is the essence of variable rate technology (VRT) – varying fertilizer, seed, or pesticide rates across a field based on local needs rather than a uniform blanket application. As farm sizes grow and margins tighten, agronomists and producers are increasingly turning to high-resolution data to drive these VRT decisions.

Drones for farming equipped with multispectral sensors have emerged as a powerful tool to capture that data. By crop scouting with multispectral cameras, growers can quickly map crop health variability using indices like NDVI. These aerial insights form the foundation for precise management zones and prescription maps that guide both aerial and ground-based application systems.

In fact, the latest drone and software combinations have made it easy and affordable for virtually anyone to collect detailed crop imagery and get a complete picture of in-field variations. The result? Better-targeted inputs, reduced waste, and improved yields – all through data-driven decision making.

Understanding Variable Rate Technology (VRT) in Agriculture

Variable rate technology refers to any system that allows application of inputs (fertilizer, seed, irrigation, pesticides, etc.) at varying rates across different parts of a field. Instead of treating the field as one homogeneous unit, VRT recognizes that soil properties, crop vigor, pest pressure, and other factors can vary dramatically within a few acres.

Traditionally, farmers acquired VRT prescriptions from sources like grid soil sampling or yield maps from combines. While these methods do provide valuable information, they often lack spatial resolution or timeliness. For example, soil sampling might take one sample per hectare and require interpolation for the areas in between , and yield maps only reflect end-of-season outcomes. What’s needed is a more immediate and finer-scale view of crop health during the growing season – and that’s where drone-based multispectral data comes in.

By capturing multispectral imagery from the air, drones can detect subtle differences in plant conditions that are invisible to the naked eye. These differences are quantified through vegetation indices (like NDVI, NDRE, and others), which serve as proxies for crop vigor, biomass, or nutrient status.

High-resolution index maps enable farmers to delineate management zones within fields with precision. In essence, aerial multispectral data provides the agronomic intelligence that powers VRT. Without it, a farmer might default to uniform treatment – say, 200 lbs of nitrogen per acre everywhere – potentially over-applying in some spots and under-applying in others. With it, the same field can be divided into zones that receive, for example, 150, 200, or 250 lbs of N based on need, or certain patches can be skipped entirely if they don’t need treatment.

This level of precision was difficult to achieve before affordable drone technology; now, it’s within reach for medium to large farm operators and even custom drone service providers who can offer VRT mapping as a service.

NDVI and Plant Health Indices: Enabling Precise Field Zonation

At the heart of drone-based VRT is the plant health map – typically an NDVI map or similar index map of the field. NDVI (Normalized Difference Vegetation Index) is a ratio of reflected near-infrared (which healthy leaves strongly reflect) to red light (which healthy leaves absorb). It ranges from –1 to +1, where higher values usually indicate denser or more vigorous vegetation. An NDVI map essentially highlights which areas of a crop are thriving and which are struggling. Other indices like NDRE (Normalized Difference Red Edge) focus on the red-edge portion of the spectrum, useful for detecting subtle changes in chlorophyll that correlate with nitrogen status. For instance, NDRE can often catch nitrogen deficiencies earlier than NDVI, since it is sensitive to leaf chlorophyll content. Indices such as OSAVI or GNDVI provide alternatives to account for soil background or use different band combinations, but NDVI remains the go-to index for a general crop vigor assessment.

Index	Description	Use Cases	Formula
NDVI	Normalized Difference Vegetation Index: Measures vegetation health by comparing NIR and Red light.	Identifying healthy vegetation vs. stressed or dead plants.	(NIR - Red) / (NIR + Red)
NDRE	Normalized Difference Red Edge: Focuses on chlorophyll content in leaves.	Early detection of nitrogen deficiencies.	(NIR - Red Edge) / (NIR + Red Edge)
OSAVI	Optimized Soil Adjusted Vegetation Index: Reduces soil brightness interference.	Assessing sparse vegetation and soil conditions.	(NIR - Red) / (NIR + Red + 0.16)
GNDVI	Green Normalized Difference Vegetation Index: Uses Green and NIR to highlight plant vigor.	Monitoring chlorophyll and nutrient uptake.	(NIR - Green) / (NIR + Green)

By processing drone images into these indices, agronomists can produce a color-coded map of crop health. Areas in green on an NDVI map typically indicate healthy, vigorous growth, while yellow-red areas show stress or poor growth (and very low or negative values may indicate bare soil or dead vegetation). These maps enable precise zonation – dividing the field into management zones based on relative crop performance. Rather than guessing where the crop is doing well or poorly, the NDVI map provides objective, georeferenced data.

Real-world examples illustrate how valuable these zones can be. In one case, a 35 ha winter wheat field was flown by a drone in early spring to assess establishment before the season kicked into high gear. The NDVI map revealed significant variation: portions of the field on clay-rich soils had thin crop stands (lower NDVI) due to dry conditions the previous fall, whereas patches on lighter soil had a denser canopy (higher NDVI) early on. However, those very lush patches on sandy soil were likely to suffer moisture stress later, limiting their yield potential. Armed with this insight, the agronomist adjusted the seeding rate for the next crop. Areas that had been overly dense (but with lower eventual yield potential) were sown with fewer seeds (35 plants/m²), while the thinner areas on heavy soil were sown with more seeds (60 plants/m²) to boost their productivity. The remainder of the field stayed at the normal 50 plants/m² rate. These plans were converted to seeding rates in kg/ha and, with a click of a button, exported as a shapefile ready to load into the variable-rate seed drill. In essence, the NDVI-driven zone map directly informed a variable seeding prescription, tailoring plant population to soil and growth conditions. This kind of fine-tuned management would be impractical without a drone-generated map; the aerial multispectral data served as the foundation for a data-driven seeding strategy.

Importantly, NDVI and other index maps not only show where plants are weak or strong – they also often hint at why. Field knowledge combined with these maps helps diagnose issues: a persistent wet spot, nutrient deficiency, pest infestation, or soil texture change can all manifest as patterns in an index map. Drone-based scouting throughout the season can therefore guide decisions like whether an area needs extra fertilizer, if a fungicide should be applied only in pockets of disease, or if a section of the field should be replanted. By capturing problems early and precisely, farmers can intervene while the crop is still salvageable, rather than discovering issues at harvest when it’s too late. High-resolution imagery ensures even small problem areas (missed by coarse satellite pixels or ground scouting on foot) are identified. This level of insight is what makes multispectral drone data a powerful driver for VRT.

Drone Platforms with Multispectral Sensors

Modern multispectral agricultural drones have made it dramatically easier to collect NDVI and other plant health data. A prime example is the DJI Mavic 3 Multispectral (Mavic 3M) – a compact, ready-to-fly drone that pairs a standard RGB camera with four multispectral cameras (green, red, red-edge, near-infrared). Older platforms like the DJI Phantom 4 Multispectral paved the way, and now the Mavic 3M offers higher performance and longer flight times in a similar portable package. With up to ~40 minutes of flight time, the Mavic 3M can cover hundreds of acres per flight, using its integrated sensors to capture aligned multispectral and RGB imagery in one go. Other solutions on the market include custom rigs or fixed-wing UAVs carrying sensors like the MicaSense RedEdge or Altum, as well as offerings from senseFly (e.g. eBee). Each platform varies in coverage, price, and complexity, but the trend is clear: multispectral imaging is becoming more accessible and cost-effective for everyday farm operations.

When planning a drone mission for crop scouting and VRT data collection, there are several technical considerations to ensure quality data:

Flight Planning and Overlap

It’s critical to plan flight lines with sufficient image overlap (often 70% or more side and front overlap) so that stitching software can create an accurate orthomosaic. Many mapping apps (including DJI’s flight planner or third-party apps) help automate this. Flying at a moderate altitude (e.g. 60–120 m AGL) balances resolution with coverage – lower altitudes give finer detail (useful for detecting small weeds or early-stage issues) at the cost of more flight time. In practice, flight parameters might be adjusted based on the target: a weed mapping mission might fly lower (achieving ~1–2 cm/pixel) to spot small weed clusters , whereas a general vigor mapping over a uniform crop can fly higher (3–5 cm/pixel) to cover more area.

Timing and Lighting

To get consistent multispectral data, missions are often flown around solar noon or under uniform cloud cover. Harsh shadows or rapid changes in sunlight (e.g. scattered clouds) can introduce noise in reflectance values. Some drones like the Mavic 3M include a downwelling light sensor on top, which records ambient light during the flight to help the processing software correct for changing sun intensity. Even so, it’s good practice to avoid early morning or late evening flights when long shadows could affect the imagery.

Radiometric Calibration

Most multispectral systems come with a calibrated reflectance panel. Before or after the flight, operators take a photo of this panel under the same lighting conditions. Processing software uses this to calibrate the images so that the vegetation index values are physically meaningful (reflectance-based), minimizing variability caused by lighting differences. Radiometric calibration removes variability from changing sunlight conditions and ensures that differences in NDVI truly reflect crop conditions rather than, say, a passing cloud. This step is especially important if you plan to compare maps over time or threshold the index values for prescriptions.

Georeferencing and Accuracy

Drones like the Mavic 3 Enterprise series often include RTK (real-time kinematic) GPS for high geolocation accuracy. Accurate geotagging of images (and the resulting maps) is important if the prescription maps will be used in autosteering or precise equipment guidance. It also ensures that when you load a zone map into a tractor or a spray drone, it lines up correctly with the field. If available, use ground control points or RTK to improve map accuracy, particularly on rolling terrain or if extremely precise placement of treatments (like spot spraying individual patches) is required.

By carefully considering these factors during mission planning, you set the stage for high-quality data. When done properly, a drone flight can be very quick – often 15–30 minutes to map a medium-sized field – and the data can be processed immediately to support time-sensitive decisions.

Rapid Processing: Turning Drone Images into Actionable Maps

After the drone lands with a full memory card (or cloud upload is complete), the next step is to transform hundreds of raw images into coherent maps and analytics. This is where software platforms like Pix4Dfields and Solvi come into play. These tools are designed to ingest drone imagery and output agronomic maps (like NDVI) and ultimately prescription files with minimal user hassle.

Pix4Dfields

Pix4Dfields is an example of a fast, offline processing solution. It can stitch a set of multispectral images into an orthomosaic and calculate vegetation indices in minutes – even on a laptop in the field.

In one case, a 50-hectare wheat field was mapped and processed (both RGB and multispectral layers) in under 12 minutes using Pix4Dfields, producing detailed maps of crop variability on-site. This speed is a game-changer for in-field decision making.

Agronomists can fly a drone, process the data on a rugged computer, and discuss treatment plans with the farmer all within the same field visit. Even in remote rural regions with poor internet, this workflow holds up because Pix4Dfields does not require cloud upload – it’s optimized for offline use.

The software provides tools like the “Magic Tool” and index thresholding to quickly classify areas of interest. For example, an NDVI map can be auto-segmented into a few categories (high, medium, low vigor) using customizable thresholds or clustering algorithms. Pix4Dfields’ latest updates even include a Targeted Operations and Zonation feature that allows full control over creating management zones: users can choose the number of zones (from 1 up to 7), the algorithm for how values are binned (linear breaks, quantiles, etc.), and even refine zones manually if needed.

This means a continuous NDVI map can be turned into a discrete, management-ready zone map in a matter of clicks. An example is shown in the figure below – an NDVI crop health map on the left is converted to a 5-zone prescription map on the right for variable input application.

NDVI crop health map (left) and corresponding zonation for variable-rate application (right). Modern software can simplify a continuous index map into a set of management zones based on configurable thresholds and algorithms.

Solvi

Solvi, on the other hand, offers a cloud-based platform for processing and analysis. Users upload their drone images to the Solvi web service, which stitches the imagery and computes indices like NDVI, NDRE, VARI, or custom indices.

Being cloud-based, Solvi offloads the heavy processing to their servers – which can be convenient for users without high-end hardware, or those who want the results accessible from any device.

One of Solvi’s strengths is its focus on analytics for agronomy. For example, Solvi has a PlantAI module that can perform automatic detections (such as counting plants or identifying weed patches) on the imagery. These detections can then be converted into prescription maps for targeted applications. In practice, this means you could have Solvi not only show you an NDVI map, but also automatically highlight areas of poor emergence or patches of weeds if the situation calls for it.

In a spring scouting scenario, a weed pressure map generated via Solvi’s AI could identify clusters of weeds in an otherwise bare field; those clusters could then be directly turned into a spot-spraying prescription file. Solvi also makes generating the final application files straightforward – as noted earlier, a shapefile for a seeder or spreader can be exported with one click , and its Plant Health mapping feature lets users easily translate an index map into a variable-rate file for fertilizer spreaders.

It’s worth noting that these platforms (and others like DroneDeploy, Agremo, Sentinel Hub, etc.) each have their niche. Pix4Dfields is often favored for speed and data ownership (no internet required), whereas Solvi is valued for advanced analysis and continuous updates/improvements in the cloud.

Some users even use both – processing in Pix4Dfields for quick turnaround, then uploading results to a cloud platform for archiving, sharing, or further analysis.

Ultimately, whether you choose an offline solution or a cloud-based one, the goal is the same: turn raw drone data into a diagnostic map and then into a prescription map. The faster and easier this process is, the more readily it fits into the tight timelines of farm decision-making.

From Maps to Action: Creating Prescription Files

Once a field’s zones have been defined (whether it’s two zones or seven zones), the next step is assigning the actual input rates and creating the prescription file (sometimes called an application map). This is essentially a digital map layer, often in formats like Shapefile, KML, or ISOXML, that contains the polygons or grid cells of the zones with an attribute for the application rate (e.g. “Zone A: 50 kg N/ha, Zone B: 80 kg N/ha”, or “Spray Zone 1: 8 L/ha, Zone 2: 0 L/ha”, etc.). Modern farm software and equipment consoles can ingest these files to drive variable rate controllers.

The process of generating a prescription is usually integrated into the software we highlighted earlier:

Pix4Dfields Workflow

In Pix4Dfields, the Targeted Operations wizard guides the user to assign values to each zone after performing zonation. For example, after segmenting an NDVI map into 3 classes, you might input the desired fertilizer rate for low, medium, and high vigor zones. Pix4Dfields then produces a map layer with those rates. Impressively, Pix4Dfields 2.5 introduced direct export to John Deere systems via the cloud (more on that shortly) and also the ability to include no-spray zones and obstacles in the prescription.

That means if there are areas in the field that must be avoided (waterways, sensitive habitats, etc.), those can be marked and will be recognized by the machinery so it automatically turns off application in those zones. The software can also simulate input savings; for instance, Pix4Dfields can calculate how much chemical will be used in a spot-spray scenario versus full cover, so you know the cost savings before even leaving the office.

Solvi Workflow

In Solvi, after you have an index map, you can use the Plant Health Maps tool to choose an index threshold or segmentation method and assign rates accordingly. Solvi’s examples show similar workflows for different use-cases: e.g., converting a vegetation map of an oilseed crop into a nitrogen prescription file for a spreader, where high NDVI areas get less N and low NDVI areas get more.

 The platform then lets you download the prescription in common formats compatible with machinery. If multiple brands of equipment are used in an operation, Solvi’s standard shapefile outputs are broadly compatible, and Pix4Dfields’ support for ISOXML covers a wide range of manufacturers thanks to the ISOBUS standard (ISO 11783). In fact, Pix4Dfields can now export ISOXML packages that have been tested to work with equipment terminals from major brands like John Deere, Case IH, New Holland, AGCO, CLAAS, Trimble, Amazon, Topcon, Kubota, and more.

This cross-brand compatibility is crucial – it means the maps you create from drone data aren’t locked to one kind of tractor or sprayer, but can be used almost universally with modern precision ag hardware.

Delivering the Prescription: Integration with Equipment and Systems

The end goal of all this data processing is to actually implement variable-rate application on the ground or by air. Integration with machinery is therefore a key part of the workflow. There are two primary routes for applying a drone-derived prescription map: aerial application via spray drones, or ground application via tractors/sprayers/spreaders. Let’s examine each:

Aerial Application with Spray Drones

Specialized agriculture drone sprayers (often called spray drones or UAV sprayers) can take the prescription map and execute it by autonomously flying and spraying the designated rates.

An example is the DJI Agras series of spray drones (such as the Agras T40, T30, etc.), which are capable of carrying tens of liters of liquid and spraying crops or fields. The combination of a mapping drone like the Mavic 3M with a spray drone creates a powerful closed-loop system. In fact, DJI explicitly enables this: after using a Mavic 3 Multispectral to scan a crop, a prescription map can be generated (either in DJI’s Terra software or a third-party tool) and then uploaded to the Agras drone for mission execution.

During a prescription spraying mission, the drone’s flight controller will vary the flow rate of the sprayer (and sometimes the flight speed) according to the map’s specifications – applying a higher dosage on affected low-NDVI areas and a lower dosage on healthier high-NDVI areas. All of this is automated; the operator just monitors the drone. This approach has been proven to greatly reduce chemical use and increase precision.

For example, in a spot spraying trial in Hungary, a team used a DJI Phantom 4 Multispectral to map weed-infested patches of a weed called Cirsium arvense and then targeted those patches with a DJI Agras spray drone. Compared to a traditional blanket herbicide application, the drone-based prescription approach used 67.8% less herbicide, saving about 14.57 EUR/ha in chemical costs.

Only the weed patches (identified via NDVI maps since the field was otherwise bare) were treated, with three different dose levels applied depending on infestation severity. The prescription map was sent from the mapping software (DJI Terra in that case) directly to the spray drone, which then carried out the variable-rate treatment precisely on those spots. This example underscores how agriculture drone spraying can become smart and selective instead of blanket coverage – effectively implementing VRT from the air. As regulations and technology for drone spraying advance, we can expect to see more spraying drones integrated into farm operations, especially for spot treatments, small fields, or areas difficult for ground rigs to reach.

It’s important to note that not all spray drones currently support dynamic rate changes “on the fly” in a single mission – some simpler systems might require segmenting a field into separate block missions if different rates are needed.

However, leading systems like DJI do offer true variable rate spraying based on prescription maps. And beyond DJI, other manufacturers (such as XAG or Hylio) are also enabling prescription-based drone spraying. From an agronomist’s perspective, the key is that the data and prescription we create should be in a format the drone can use. This typically means using the manufacturer’s software ecosystem or ensuring the third-party software exports in a supported format (e.g., DJI Terra can import Pix4Dfields outputs, etc.).

Ground-based Application (Tractors, Sprayers, Spreaders)

For many medium-to-large farms, the primary application method is still ground equipment – like tractor-mounted fertilizer spreaders, self-propelled sprayers, planters, etc. These machines, when equipped with rate controllers and GPS, can execute variable rate prescriptions by changing seeding rate, spray volume, or fertilizer flow on the go as they move through different zones. To use a drone-derived prescription with ground equipment, the digital prescription file must be transferred into the farm’s precision ag system.

Historically, this was done by copying the file to a USB stick and physically transferring it to the machine’s cab. For instance, an agronomist might email a shapefile to a farmer who then loads it onto a USB and imports it into their tractor’s monitor (like a John Deere GreenStar display). In the winter wheat use-case we discussed earlier in Sweden, that’s exactly what happened: the consultant created a variable-rate nitrogen shapefile in Solvi, saved it to a USB memory stick, and the farmer loaded it into the tractor’s GPS receiver for spreading the fertilizer. The prescription effectively told the spreader how much N to apply in each part of the field – with the result that the average N rate was 70 kg/ha instead of a blanket 80 kg/ha, saving about €200 on that application, and likely improving overall yield by avoiding over- and under-fertilization.

Today, we have even smoother ways to integrate. John Deere Operations Center is a cloud platform that many farmers use to manage field data and send prescriptions to equipment wirelessly. Recognizing the importance of this, Pix4Dfields introduced a direct integration that allows users to upload maps and prescriptions straight to a John Deere Ops Center account from within Pix4Dfields. No more USB stick hassle – as long as you have internet, you can click a button and send the files to the tractor cab over the air.

This creates a unique, seamless workflow where a drone map made in the morning can be in the John Deere in-cab display by the afternoon, ready for application. Many other farm equipment brands enable similar connectivity through their cloud platforms or ISOBUS compatibility. The key standard here is ISOBUS/ISOXML, which is an ISO standard for agricultural data sharing. If your prescription is exported as ISOXML, it can be read by most modern terminals (the list includes not just John Deere, but Case IH’s AFS, New Holland’s IntelliView, AGCO’s monitors like those in Fendt or Massey Ferguson, Trimble, CLAAS, Amazone, Topcon, etc. – essentially any system adhering to ISO 11783). This standardization effort means that as long as your software exports a compliant file, you don’t have to worry about the brand mismatch; a mixed fleet can still utilize the same drone-derived prescription map.

Practical Workflow: From Drone Flight to Field Action

To tie everything together, let’s walk through a typical workflow of using drone multispectral data for a VRT operation on a farm:

1. Planning – The agronomist or drone service provider meets with the farmer to identify the goal (e.g., variable-rate fertilization for mid-season corn, or a fungicide spot application in wheat, or mapping crop vigor for variable-rate seeding next season). They choose the appropriate timing (crop growth stage) and ensure the drone equipment (say a DJI Mavic 3M) is ready, with batteries charged and a flight plan covering the field with proper overlap.

2. Data Acquisition (Flight) – At the field, ground control points are laid out if extreme accuracy is needed (often not necessary for just prescription maps, as relative accuracy suffices). The drone is launched to autonomously capture the multispectral images. For example, it flies a lawnmower pattern over a 100-acre field at 120 m altitude, taking overlapping photos. If it’s a multirotor like the Mavic, it might take 25–30 minutes and several battery swaps to cover that area. Each image has embedded GPS coordinates, and the drone’s sunlight sensor logs illumination data. Immediately after the flight (or even mid-flight if processing concurrently), a calibration target image is taken and the data is offloaded to a laptop.

3. Processing & Analysis – The images are loaded into Pix4Dfields on the laptop. Within 10–15 minutes, the software produces an orthomosaic and an NDVI map of the entire field. The agronomist quickly checks the map for quality (coverage complete, no major stitching errors). Let’s say the NDVI map shows some clear patterns – perhaps the west side of the field is showing lower NDVI due to known sandy patches, and a few streaks of low NDVI align with where an irrigation issue occurred. Using the software’s zonation tool, the agronomist decides to create 4 zones: very low, low, medium, and high vigor. Pix4Dfields segments the map accordingly and shows an overlay of colored zones. The agronomist then assigns fertilizer rates: for example, 0 kg/ha extra N for the high vigor zone (the crop is lush enough), 30 kg/ha extra for medium, 60 kg/ha for low, and 90 kg/ha for very low vigor areas (essentially a rescue dose for poor areas). These numbers come from his agronomic knowledge and perhaps some reference like tissue tests or yield goals. The software generates the prescription map with these values embedded.

4. Validation (Optional) – If time permits, they might ground-truth a couple of spots: e.g. drive to a “low NDVI” spot to confirm the crop indeed looks weaker or nitrogen-deficient. This can be done by loading the NDVI map on a mobile device or using GPS to navigate to the coordinates. Upon validation, the prescription is adjusted if needed (perhaps one zone was low NDVI due to flooding where adding N won’t help, so instead that zone might be left with a maintenance dose or skipped).

5. Deployment to Equipment – Now satisfied, they export the prescription. Using Pix4Dfields’ integration, they click “Upload to John Deere” and send the map to the farm’s John Deere Operations Center account. A few minutes later, the farmer confirms on his John Deere display that the map for “North_100ac_Field – Topdress Nitrogen” is received. The map is then loaded into the spreader’s task list. Alternatively, if the farm had a different brand, the agronomist might export an ISOXML file to a USB drive. If an aerial application was planned instead (say with a drone sprayer), the prescription might be exported as a DJI Terra-compatible file or simply as a shapefile which the drone’s app can import – then the operator would set up the spray drone mission accordingly. In either case, the VRT prescription is now in the hands of the machine that will do the work.

6. Field Application – The tractor (or spray drone) carries out the variable-rate application following the prescription map. For a tractor spreader, this means as it drives across the field, its GPS position is constantly cross-referenced with the georeferenced prescription; the controller modulates the fertilizer flow rate on the fly to match the target for that zone. The farmer might observe that the spreader is flinging more fertilizer in the visibly weaker areas and less in the lush areas – exactly as intended. If it were a spray drone doing a spot spray, it would only turn on its spray when flying over the mapped weed patches, and turn off or reduce flow elsewhere.

7. Post-Application Analysis – The operation might generate a log or “as-applied map” from the equipment, which can be compared to the prescription. Additionally, in subsequent weeks, the agronomist might fly the drone again to see the impact – ideally, the zones start looking more uniform after the intervention. Over the season, yield monitors will tell if the VRT approach paid off in terms of output. These results can refine future decisions (creating a data feedback loop).

This workflow highlights the practical, field-level use of drone-based multispectral data. It’s a blend of agronomy, technology, and logistics. By following a structured process, even large fields can be mapped and treated in a targeted manner within a single day. Drone service providers often operate in this way to deliver value-added services to clients (the farmer doesn’t even necessarily need to know the nitty-gritty of NDVI – they just see the outcome of “we saved X amount of fertilizer and the crop looks better”).

Considerations and Best Practices for Success

Implementing drone-based VRT does come with considerations. Here are some best practices and technical tips for success:

Know Your Objective

Always be clear on what decision or action will come from the drone data. The approach for mapping to guide nitrogen topdress will differ from mapping to decide replant vs not, or mapping for a fungicide application. Knowing the end goal helps in choosing the right timing, index, and analysis. For example, NDRE might be favored for mid-season corn fertilizer decisions (due to its sensitivity to chlorophyll/nitrogen in high-biomass conditions), whereas NDVI or VARI might be better to find weeds on bare soil (where green weeds pop against brown dirt).

Combine Data When Possible

While drone NDVI is fantastic, it can be even more powerful when combined with other layers. Yield maps, soil electrical conductivity maps, elevation/topography, and historical satellite imagery can all enrich the interpretation. Many agronomists use drone data to identify zones but then use agronomic knowledge or soil data to decide why a zone is the way it is. For instance, a low-NDVI zone might be low due to sandiness – which means adding more fertilizer might not help much (the issue is water holding capacity, not lack of N). In that case, maybe the VRT decision is to plant a different hybrid there or improve irrigation, rather than just dumping more inputs. Field context is key. Use drone maps as a guide, but ground-truth or consult experience for the smartest prescriptions.

Data Quality Checks

Ensure the drone data is properly calibrated and processed. Watch out for stitching artifacts (which can happen if overlap is low or if the field has uniform texture with few features). These could create false zones. Most processing software will flag if there were issues (blurry images, etc.). It’s good practice to preview the orthomosaic and index map for any obvious errors before generating zones. If something looks off (like a strange striping or sudden NDVI jumps that don’t align with reality), investigate and potentially re-process or re-fly if needed. Flying with consistent settings (same camera exposure settings across the flight, if manually controllable) can also improve consistency.

Resolution vs. Practicality

Drone maps can be extremely high-resolution (sub-inch), but your prescription doesn’t necessarily need to vary at that micro level – most equipment can’t switch rates every few inches of course. Don’t overcomplicate the zone map; choose a reasonable number of zones that match the scale of meaningful variability. Simpler prescriptions are often easier to execute and manage. For example, splitting a 100-acre field into 4 zones is usually quite adequate to capture major variability, whereas trying to have 10-15 zones might be overkill and difficult to calibrate equipment for each tiny area. Use the software tools to smooth or cluster small erratic patches if they aren’t significant.

Regulatory Compliance

If using spray drones (or even VRT fertilizer spreading), be mindful of regulations. Some regions have laws about drone spraying or require certain certifications to apply chemicals by drone. Always operate within those rules. Also, when reducing chemicals, ensure it’s agronomically justified – the goal is to use less without risking efficacy. The drone map might show a few weeds, but make sure the threshold set for spraying vs not spraying won’t leave an untreated weed problem. This is where agronomist expertise comes in.

Training and Skills

There is a learning curve to using drones and processing software effectively. Invest time in training – many platform providers offer tutorials (for instance, Pix4D and Solvi have online courses and documentation). A skilled operator will know how to adjust flight settings or software parameters if default workflows don’t yield good results. Over time, patterns emerge (e.g., how sunlight angle affects NDVI in your area, or which index correlates best with what you’re trying to address). Keep refining your approach each season.

Cost-Benefit Analysis

For farm operators, it’s important to track the costs (drone equipment, software subscriptions, time spent) versus the benefits gained (input savings, yield increases, etc.). In many cases, the economics favor drone mapping strongly – as noted, even one prevented issue or one optimized input pass can pay off the investment. But it’s good to document those wins. For example, if you saved 85% of herbicide in a field , quantify that in dollars and communicate it. This helps in internal decision-making and justifying scaling up the practice across more acres.

Even Representation of Tools

If you're a service provider, remain flexible in tool choice. While we highlighted Pix4Dfields and Solvi, numerous tools exist and new ones emerge. Some farms might already use a certain platform (e.g., DroneDeploy or John Deere’s own software). The core principles remain the same. Ensure that whatever tool you use can be exported into a format that the farm’s equipment can read, or that you have a way to convert it. Interoperability is key to not get siloed.

By considering these factors, agronomists and farmers can maximize the value derived from drone-based VRT. It transforms the drone from just a cool gadget into a decision-making powerhouse on the farm.

In Conclusion

Drone-based multispectral data has firmly established itself as the bedrock of modern variable rate technology in agriculture. By providing timely, high-resolution insights into crop health through NDVI and other indices, drones empower agronomists, crop consultants, and farmers to move from one-size-fits-all farming to site-specific management.

The synergy of tools – reliable UAV platforms like the Mavic 3 Enterprise series that capture quality data, powerful software platforms like Pix4Dfields and Solvi that turn data into actionable maps, and advanced application systems like smart tractors and spray drones – has made precision agriculture more accessible than ever to medium and large-scale operators.

In conclusion, using aerial multispectral data as the foundation for variable rate technology isn’t just about cool maps – it’s about making better decisions at the field level. It’s the bridge between observing variability and doing something about it. By following best practices in data collection, processing with the right tools, and seamlessly bringing prescriptions to application equipment (be it a spraying drone or a high-tech tractor), precision agriculture professionals can significantly enhance productivity and sustainability. The fields may be variable, but with the right data, our management can be variable and precise – delivering the right input, to the right place, at the right time.

References

• Pix4D (2024). 85% Input Savings with PIX4Dfields & DJI Mavic 3 Multispectral – Case study of drone mapping and spot-spraying herbicide in Germany.

• Pix4D (2023). PIX4Dfields 2.5: John Deere Integration and ISOBUS Export – Blog post on new features allowing direct upload of drone-derived maps to John Deere Operations Center and export in ISOXML for various brands.

• Solvi (2019). Variable Rate Seeding with Drone Imagery – Blog post detailing how drone NDVI maps from a previous crop were used to create a variable-rate seeding prescription (shapefile) for the next crop.
  

• Solvi (2024). 5 Ways to Use Drones for Spring Crop Scouting – Article covering use-cases like prescriptive nitrogen maps and weed spot-spraying with drone imagery; mentions converting PlantAI detections to prescription files for spreaders and sprayers.
  

• Solvi (2019). Better Nitrogen Application in Winter Wheat with Drones – Case study of a Swedish farm where drone NDVI mapping enabled a variable-rate nitrogen application, saving fertilizer and improving crop management.
  

• Advexure (2025). DJI Mavic 3 Multispectral: Top Precision Ag Drone in 2025? – Blog post on precision ag drones; discusses use of multispectral data for variable-rate applications and reports input cost reductions of 10–15% from VRT fertilization in trials.
  

• DJI Agriculture (2023). Spot Spraying with Drones Case Study (Hungary) – Describes using a Phantom 4 Multispectral and Agras drone for spot herbicide application on Cirsium weed patches, achieving ~68% reduction in herbicide use.