What is NDVI and how does it detect crop disease?

NDVI (Normalized Difference Vegetation Index) measures the ratio of near-infrared to red light reflected from plant surfaces. Healthy vegetation reflects near-infrared light strongly — stressed or diseased tissue does not. This makes physiological stress visible in NDVI output before any symptom appears in standard RGB imagery.

Product news | Agrolab

Q: Which crops and diseases can be monitored using NDVI drones?

While this article focuses on potato blight (Phytophthora infestans), NDVI-based drone monitoring is applicable across a range of fungal and stress-related conditions in arable crops. Contact Agrolab to discuss monitoring programs for specific crops and pathogens.

Digital PPP Labels in the EU by 2027? What Companies Should Prepare for Now

Sarah Hubert — Wed, 08 Apr 2026 07:00:56 +0000

Digital PPP Labels in the EU: What Product Owners Should Prepare for Now

8 April 2026

Digitalisation of plant protection product (PPP) labels is moving from industry initiative to becoming a regulatory requirement.

The European Commission is preparing new labelling rules that will require all PPPs to have both a physical label and a digital, machine-readable version accessible via a link, a QR code or similar technology.

Regulation (EU) 2024/2865 entered into force in October 2025 and covers several updates to the classification, packaging and labelling requirements for PPPs. Some requirements are implemented on July 1, 2026, while others have a transition period until July 1, 2028. The deadline for having digital labels available is January 2028.

At the same time, platforms such as AgriGuide, are being developed to support implementation across EU Member States. The platform is backed by CropLife Europe and COPA-COGECA and is already being piloted in several countries.

Many large crop protection companies are already working towards full digitisation of their label portfolios by the end of 2026. This development affects how PPP labels are structured, maintained and used in practice.

Visual mockup of label scanning app. For illustrative purposes only.

What is a digital PPP Label?

A digital PPP label is a machine-readable version of the authorised product label that can be accessed online and interpreted by software systems.

Instead of reading a static document, users can access structured label data that integrates with:

Local regulatory restrictions
Buffer zones and environmental requirements
Crop and application context

Through tools such as AgriGuide, this allows users to receive application guidance aligned with the authorised conditions of use.

Digital labels can also support automatic record-keeping in line with EU requirements.

EU regulatory requirements for digtial labels

The upcoming EU framework introduces several key requirements for PPP label digitalisation:

Labels must exist in both physical and digital formats
Digital labels must reflect the authorised content of the product
They must be freely accessible without login or payment
Access must be possible via a single scan or link from packaging
Digital labels must be kept up to date when authorisations change

These requirements are expected to replace current provisions under Commission Regulation (EU) 547/2011.

Digital labels structure: content, data model and access

PPP label digitalisation operates across three main layers:

1. Content layer
All authorised label elements must be complete and aligned with the registration:

Crops and targets
Application rates and timing
Safety instructions and PPE
Buffer zones and environmental restrictions

2. Structure layer
The content must be encoded in a structured, machine-readable format so that software can interpret individual data fields such as crop, dose, interval, and conditions.

3. Access layer
The digital label must be linked to the physical product via a QR code or similar identifier and hosted on a platform with version control and traceability.

What digital labels mean for product owners

For product owners and registration holders, digital labels introduce new operational requirements.

Consistency across markets
Label data must be harmonised to function correctly in digital systems.

Precision in authorised content
Ambiguity in label wording can lead to incorrect interpretation by software tools.

Continuous updates
Digital labels must be updated and synchronised when authorisations change.

This shifts label management from a documentation task to a structured data process.

Preparing for implementation

With regulatory changes happening, the industry is already moving towards full digitisation as preparation needs to happen now.

For most organisations, this involves:

Reviewing label content for completeness and consistency
Structuring data in a machine-readable format
Aligning regulatory, technical, and product data workflows
Ensuring processes are in place for ongoing updates and version control

This should not be seen as a one-off transition, but the establishment of a new operating model for label management.

Digital PPP labels represent more than a regulatory update. And it's already on the way.

If you are currently evaluating how to structure your label data or align it with upcoming requirements, it is worth starting that work well ahead of the transition period.

FAQ

A digital PPP label is a machine-readable version of a plant protection product label that can be accessed online and used by software systems to guide application and ensure compliance.

As of now (8th April 2026) The deadline is January 2028, however some companies have already started the implementation and are aiming for 100% digital labels by the end of 2026. The details in the Regulation 2024/2865 are currentlg being discussed and therefore the deadline may be subject to change.

AgriGuide is a digital platform developed with industry and farmer organisations to host structured PPP label data and support compliant product use across the EU.

No. Both physical and digital labels will be required, and they must contain the same authorised content.

Companies need to ensure that label data is complete, structured, machine-readable, and aligned with regulatory requirements, with processes in place for continuous updates.

Explore other posts

Northern Zone Efficacy Guidance 2025 (Version 10) – Key Updates

When applying for authorization of a plant protection product (PPP) in the EU Northern Zone, applicants must follow strict efficacy trial requirements. A newly updated Version 10.0 (2025) has now been released. Learn updated PPP trial requirements to streamline EU registration. This guidance defines […]

LEARN MORE

Agrolab's Drone Technology gets GEP-Approved for Field Trials

We are proud to announce that our innovative drone-based canopy height measurement method is now officially approved by the Danish GEP inspection unit. This technology aligns with EPPO guideline PP1/333(1), which supports the adoption of digital innovations for efficacy evaluations in plant protection...

LEARN MORE

Indlægget Digital PPP Labels in the EU by 2027? What Companies Should Prepare for Now blev først udgivet på Agrolab.

Detecting Potato Blight Weeks Early: What NDVI Drone Monitoring Reveals That RGB Cannot

Sarah Hubert — Thu, 05 Mar 2026 15:13:16 +0000

Detecting Potato Blight Weeks Early: What NDVI Drone Monitoring Reveals that RGB cannot

A 9-week field monitoring case using drone-acquired NDVI imagery, and what it means for trial design, early intervention and crop protection decision-making.

What the human eye misses

Standard RGB drone imagery captures what you would see from the air with your own eyes: color, texture, and visible damage. For many applications, that is sufficient. For early disease detection in agricultural field trials, it is not.

Vegetation indices such as NDVI (Normalized Difference Vegetation Index) work differently. Rather than looking at the combined wavelength, which gives you the same visible impression a normal RGB picture does, we look at the relationships between reflected light at different wavelengths, both visible and non-visible. NDVI looks at the relationship between reflected red light and near-infrared light. If there is low reflected light in the red band and high reflected light in the near-infrared, it can explain how effective photosynthesis is in the plant. This difference becomes measurable, mappable, and actionable in NDVI output, even when it is completely invisible in standard imagery.

The practical result is significant. NDVI can reveal physiological stress in crops before any visible symptom appears. For fungal diseases such as Phytophthora infestans, the pathogen responsible for potato blight, this detection window can open several weeks ahead of what RGB imagery or direct field observation would show.

NDVI can detect physiological stress in potato crops several weeks before symptoms become visible to the naked eye or in standard RGB imagery.

Nine weeks of Potato Blight development: What the data shows

In this monitoring series, drone flights covered a potato trial field at regular intervals across nine weeks. Each flight captured both standard RGB and multispectral imagery processed to NDVI output. The two datasets were then aligned and compared week by week.

In the early weeks, RGB imagery showed a uniform, apparently healthy crop. The NDVI output told a different story. Early patches of reduced reflectance appeared in areas that showed no visible symptom at all. These patches aligned precisely with areas where blight symptoms became visually apparent one to two weeks later.

By the time RGB imagery clearly indicated disease pressure, the NDVI data had already mapped the infection pattern weeks earlier. The spatial distribution, the rate of spread, and the most affected areas were all visible in the NDVI record long before standard imagery caught up.

Why Early Detection Matters for Field Trials

For crop protection field trials, accurate early disease detection is not merely useful. It is structurally important.

Trial protocols under EPPO guidelines require consistent, well-documented assessment of disease development across trial sites. Delayed detection compresses the observable window of disease progression. It reduces the statistical resolution of efficacy assessments. In some cases, a critical treatment timing gets missed entirely.

NDVI-based drone monitoring addresses each of these problems directly. By establishing a baseline early in the season and tracking deviation from that baseline week by week, teams can detect disease onset at a spatial and temporal resolution that ground-based scouting cannot reliably achieve. This is especially true across larger trial areas or multi-site programs.

What This Means for Intervention Timing

Beyond the trial context, the same capability applies directly to commercial crop protection decisions. Early NDVI signals identify which parts of a field are under stress and how rapidly that stress is spreading. This allows growers and agronomists to assess whether intervention is warranted before economic damage has occurred. That is a meaningful operational advantage in any crop protection program.

The Role of NDVI Field Trial Monitoring in Modern Trial Programs

Integrating multispectral drone monitoring into field trial design is not yet standard practice across the industry. At Agrolab, it forms part of how we approach trial execution where early disease detection, spatial variability analysis, or longitudinal crop health monitoring adds value to the data package.

The 9-week potato blight monitoring series presented here was part of our ongoing Research & Development at Agrolab. The full dataset, including raw NDVI values, flight parameters, and week-by-week comparative imagery, is available for discussion with clients and collaborators working on crop protection trial programs in the EU North Zone.

To discuss how drone-based NDVI monitoring could support your trial program, contact Martin Gejl, Innovation Manager at Agrolab: mg@agrolab.dk

FAQ

NDVI (Normalized Difference Vegetation Index) measures the ratio of near-infrared to red light reflected from plant surfaces. Healthy vegetation reflects near-infrared light strongly, where stressed or diseased tissue does not. This makes physiological stress visible in NDVI output before any symptom appears in standard RGB imagery.

In Agrolab's nine-week monitoring program, NDVI imagery identified early stress patterns three to four weeks before symptoms became visible in RGB drone footage or direct field observation.

Yes. Drone-based multispectral monitoring can be integrated into field trial programs designed to EPPO and GEP standards. It supports consistent, documented assessment of disease development across trial sites and growing seasons, adding a spatial and temporal resolution that ground-based scouting alone cannot reliably achieve. Agrolab's drone technology is officially GEP-approved by the Danish GEP inspection unit and aligns with EPPO guideline PP1/333(1) — read more about our GEP-approved drone methodology here.

While this article focuses on potato blight (Phytophthora infestans), NDVI-based drone monitoring is applicable across a range of fungal and stress-related conditions in arable crops. Contact Agrolab to discuss monitoring programs for specific crops and pathogens.

Our drone monitoring program is led by Martin Gejl, Innovation Manager at Agrolab's R&D department. For specific equipment and sensor details relevant to your trial requirements, contact Martin directly at mg@agrolab.dk.

The North Zone's distinct growing conditions — shorter seasons, specific pest pressure windows, and variable weather across Denmark, Sweden, Finland, and the Baltic states — make early detection particularly valuable. Drone monitoring allows consistent tracking across geographically dispersed trial sites, supporting the multi-site, multi-season data requirements of North Zone registration under Regulation (EC) No 1107/2009.

NDVI is one of many vegetation indices and measurements drone technology can capture. The right output depends entirely on your trial objectives. Some examples of services we offer are canopy height, crop volume, green leaf area, and crop cover, but drone-based monitoring can be tailored to measure whatever your program requires. Contact Martin Gejl to discuss what makes sense for your specific trial design mg@agrolab.dk

Explore other posts

Northern Zone Efficacy Guidance 2025 (Version 10) – Key Updates

LEARN MORE

Agrolab's Drone Technology gets GEP-Approved for Field Trials

LEARN MORE

Indlægget Detecting Potato Blight Weeks Early: What NDVI Drone Monitoring Reveals That RGB Cannot blev først udgivet på Agrolab.

Logarithmic trial designs: A useful tool

Kenneth Sørensen — Tue, 31 Jan 2023 13:09:18 +0000

Logarithmic trial designs: A useful tool for accurate PPP dose-responses from large data sets

Background: Image technology and drones can provide high-resolution pictures and assessments of all plots in a trial, a feature that is difficult when standing on the ground conducting visual assessments. Digital imaging is especially useful for assessing products that change crop color, cause desiccation, greening, crop stand changes etc.

Plant protection product development and registration requires detailed information on the products dose-response relation. The objective of this posting is to introduce the possibility of getting valuable data from a small trial with 3 replicates using drone images and logarithmic sprayers.

Methodology: The drone image and figure 1 below show the results from a cycloxydim application to a spring barley crop. We applied two formulations of cycloxydim. The application was made at BBCH 24 on the 23^rd of June 2022. The trial was made with 3 replicates, and the drone images were taken 28 days after application. The methodology of the logarithmic sprayer is to start with an initial high dose of 400 g cycloxydim/ha and to decrease subsequent doses in a logarithmic manner, ending with a final application of close to zero g/ha.

Figure 1: Drone image of the trial.

We used the open-source program R to analyze the trial. To extract the data from the images, we used FieldImageR (https://github.com/OpenDroneMap/FIELDimageR) which is developed for analyzing drone images of field trials. For the statistical modeling of the dose-response curves, we have used the R package DRC (https://cran.r-project.org/web/packages/drc/index.html) (see the also (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146021).

Each plot in the trial was divided into 50 subplots which have a length of 40 cm, and the center dose of each plot was calculated. The first 80 cm and the last 80 cm were not included in the calculation to avoid the effect of border spraying. This gave 46 doses to model the dose-response curves. The number of doses you can retrieve of such an Image is shown in Figure 2.

Figure 2: Data for the 2 treatments plotted on a logarithmic scale.

With a normal RGB image you can calculate different vegetation indexes. We used the index Excess Green, which is 2 * Green band – red band – blue band. Numerous other indexes can be calculated from the RGB bands, and even more indexes are possible if you have infrared.

Given that the two treatments we compared are the same active, just different formulations, we will assume that the products have the same upper and lower limit. The data indicates there could be hormesis (a small amount of the active gives a positive plant growth) which is not uncommon for herbicides, and the DRC package allows you to model the dose-response curve both with and without hormesis (se figure 3).

Figure 3: Dose-response modeling of two formulations of cycloxydim. The left side shows the curve with the assumption of no hormesis, and the right graph shows the curves with an assumption of hormesis.

Results and Conclusion: With the program, it is possible to calculate different ED_x (effective dose) values. We calculated the values and presented them with a 95% confidence interval. In table 1 you can see the ED₅₀ values estimated for the dose-response curves with and without hormesis.

ED₅₀ estimate without hormesis
Product	ED₅₀ estimate	Std. error	Lower 95% confidence	Upper 95% confidence
Formulation A	99.8361	3.3422	93.2560	106.4162
Formulation B	119.3249	4.7821	109.9100	128.7398
ED₅₀ estimate with hormesis
Product	ED₅₀ estimate	Std. error	Lower 95% confidence	Upper 95% confidence
Formulation A	120.126	10.331	99.786	140.466
Formulation B	146.158	13.511	119.557	172.759

Table 1: Results from the DRC model of the dose-response curves fitted with and without hormesis in g ai/ha.

The advantage of using logarithmic trials for analyzing dose-response relations is obvious. In a plot where we sprayed 60 m², we get an unlimited number of doses. If this had been done with a normal randomized block design, we would have needed at least 6 well-chosen doses to model the curve, and this would require larger trials and would be more time consuming. With the logarithmic trial, you only need to choose a starting dose where you are confident that you get almost 100% efficacy. Following the analyses of the dose-response curves, you can determine which doses are interesting for future investigation.

If you would like to learn more about Agrolab’s digital imaging solutions, please contact Martin Gejl (Email: mg@agrolab.dk; or Mobile +45 20736575).

Indlægget Logarithmic trial designs: A useful tool blev først udgivet på Agrolab.

Snow Mold Trials on Golf Course

Kenneth Sørensen — Fri, 07 Oct 2022 12:43:33 +0000

Agrolab’s Team of experts are ready to conduct trials targeting snow mold on golf courses. If you would like to test your product against snow mould, or any other disease on turf, please contact Alex Schwartz at Mob: +45 60246175; Email; als@agrolab.dk

Indlægget Snow Mold Trials on Golf Course blev først udgivet på Agrolab.

Great year for carrots

Joan Grønfeldt — Fri, 05 Nov 2021 11:25:45 +0000

2021 has been a good year for carrots.

In South Sweden, the field team from Backgården was busy harvesting carrot, some very large carrots.

Indlægget Great year for carrots blev først udgivet på Agrolab.

New bee research unit a reality

Joan Grønfeldt — Tue, 10 May 2016 03:48:21 +0000

Agrolab A/S has established a new bee research unit.

Starting in the Spring 2021 season, we will be able to conduct honey, pollen and nectar residue studies in oilseed rape and other flowering field crops in 200 m2 plots.

Please do not hesitate to contact Agrolab A/S if you are interested in hearing more about residue trials or other types of GLP trials.

Indlægget New bee research unit a reality blev først udgivet på Agrolab.