phenovation-redesign

Heat stress in tomato: what the OJIP curve picks up within one day

Mon, 03 Aug 2026 15:15:49 GMT

Why look at heat stress?

Heat waves are becoming more common, and keeping crops healthy and productive during one is a big challenge. That is why breeders are looking for cultivars that are more tolerant to higher temperatures, without losing yield. But selecting the right cultivars takes a lot of time. Often, it is scored by eye, or increasingly with colour or multispectral imaging. However, in our experience, it takes two to three weeks before the plants start to show symptoms.

Photosynthesis is much faster, so chlorophyll fluorescence should pick up the stress sooner. That is what Arjen Tegels tested during his MSc internship at PhenoVation.

What we did

Tomato plants (cv. Moneymaker) were grown in a climate cabinet at 25/20 °C (day/night) and 300 µmol m⁻² s⁻¹ of white LED light. After a week of baseline measurements, we raised the temperature to 35/24 °C.

Every day, we measured the plants with a PlantExplorer, fitted with a faster camera (minimal 333 frames per second). This allowed us to image the OJIP rise (see our OJIP posts, part 1 and part 2) across the whole plant, instead of at one spot on one leaf. From each curve, 28 parameters about photosynthesis were calculated.

Dark-adapted plants were measured after 30 minutes, 5 hours and 7 hours of darkness. Light-adapted plants were measured at dawn and at the end of the day. The same plants were also imaged for colour and multispectral (hue and NDVI) with a PlantExplorer PRO+.

OJIP picks up stress within one day

After a single day at 35 °C, the chlorophyll fluorescence images already showed heat stress. Colour and NDVI did not show anything in the full 10 days.

Hue and NDVI actually increased, which would suggest greener, healthier plants. Some leaves did curl, however. This showed their lighter underside to the camera, which shifts the colour and NDVI values slightly.

δRo and PItotal: the clearest signal

δRo, the efficiency with which electrons reach the end acceptors of photosystem I, dropped after one day of heat. It did so at all three dark-adaptation times, just as fast after 30 minutes of darkness as after 7 hours.

PItotal (which includes δRo) also dropped after one day when measured after 5 or 7 hours of darkness. After 30 minutes of darkness, it took two days. Both parameters stayed clearly below the baseline for the rest of the treatment. By day 10, δRo was about 20–35% lower and PItotal about 30–60% lower, depending on the dark-adaptation time.

Fv/Fm and PIABS missed it

Fv/Fm is the most used stress parameter, but it barely moved. It even went up slightly in the first days of heat, and PIABS rose at first too.

The difference between PIABS and PItotal is exactly the δRo parameter. That suggests heat hit the end of the electron transport chain, after photosystem II, which Fv/Fm does not look at.

In light: also look at the spread of the pixels, not only the mean

In light-adapted plants, the average Fq'/Fm' (ΦPSII) did not change. However, the images told a different story. Some leaves had much lower efficiency, while others stayed the same or even improved. The average hid that effect.

So instead of the mean, Arjen also looked at the histogram, specifically, how far the 12.5th percentile (lowest part of the data) sits below the median. At the end of the day, that spread doubled from 0.08 to 0.16 after a single day of heat. At dawn, it took three days, probably because damage builds up over the day.

What to take from this experiment

Chlorophyll fluorescence imaging picked up heat stress in tomato within one day. Colour and NDVI did not show it within ten days.
δRo and PItotal gave the clearest signal. Fv/Fm alone would have missed it.
In light, look at how the values are spread across the plant, not only at the average.

Read the full report here:

Vertical Phenotyping: Integrating 2D PSII Efficiency with 3D RGB Point Clouds

Tue, 16 Jun 2026 12:53:57 GMT

Climate change is pushing crops to their limits. Drought, salinity, and waterlogging are no longer rare events but recurring challenges that threaten global food security. To breed resilient varieties, we need to see what’s hidden, not just the upper leaves basking in sunlight, but the lower canopy layers where stress responses often begin. Traditional phenotyping tools, like 2D chlorophyll fluorescence imaging, provide valuable insights into photosynthetic performance (e.g., PSII efficiency, or Fq’/Fm’). Yet, they fall short in one critical way: they average values across the entire top-down view, overrepresenting upper leaves and masking the responses of lower, often more stress-sensitive layers.

This blind spot is more than a technical limitation, it’s a barrier to understanding how crops truly cope with abiotic stress. Without vertical resolution, breeders risk overlooking traits that could define the next generation of climate,resilient crops.

A novel framework developed by Vlaardingen et al. (2026) shatters this limitation by integrating 2D top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). The result? A spatial map of PSII efficiency across canopy layers, revealing vertical heterogeneity in photosynthetic activity that was previously invisible.

The alignment is remarkably precise, with R² ≥ 0.98 for x- and y-coordinates across diverse crops like quinoa, soybean, and potato. This means researchers can now;

- Visualize and quantify PSII efficiency in distinct leaf layers, from the youngest leaves to the oldest.

- Filter out non-photosynthetic tissues (e.g., flowers, seeds) that bias whole-plant averages.

- Track developmental and stress-induced changes in photosynthetic performance with unprecedented spatial resolution.

Whether it’s the early senescence of lower leaves in quinoa, the layer-specific sensitivity of middle canopy leaves in soybean under waterlogging, or the uniform systemic decline in drought-stressed potatoes, vertical resolution provides unprecedented detail, enabling breeders to target traits in vulnerable canopy layers, agronomists to refine precision management, and researchers to validate stress models with empirical, layer-specific data. In an era of increasing climate volatility, vertical phenotyping is not just an advantage, it’s a necessity for developing crops that thrive under stress.

The integration of 2D chlorophyll fluorescence imaging with 3D structural data represents more than a technical advancement, it is a shift in how we understand plant resilience. By moving beyond the limitations of flat, averaged data, this framework unlocks the layers of photosynthetic activity, offering precision in breeding, agronomy, and stress physiology research.

As climate change intensifies, spatial precision in phenotyping will be indispensable. This 2D-3D integration method not only corrects biases in traditional imaging but also scales efficiently for high-throughput applications. Its adaptability, extending to parameters like Fv/Fm, NPQ, and multispectral indices, ensures it remains at the forefront of phenotyping innovation.

Ultimately, this approach empowers us to detect early stress signals, track developmental changes, and refine crop models with a level of detail that was previously unattainable. In a world of increasing climate volatility, tools like this are not just valuable—they are essential for developing crops that don’t just survive, but thrive. The future of resilient agriculture begins with seeing the full picture.

Explore the full study: DOI:10.1186/s13007-026-01536-3

PhenoVation’s CropReporter: PhenoVation.com

NPEC’s MaxiMarvin: NPEC.nl

Are Saturating Pulses Truly Saturating?

Thu, 04 Jun 2026 09:37:53 GMT

Chlorophyll fluorescence has long been a cornerstone of plant physiology, offering a non-invasive, rapid, and portable way to assess the health and efficiency of Photosystem II (PSII). At its core lies the saturating pulse (SP), a short, intense burst of light designed to temporarily close all PSII reaction centers, enabling researchers to calculate critical parameters like effective PSII yield, electron transport rates (ETR), and non-photochemical quenching (NPQ). These metrics are indispensable for understanding how plants respond to stress, light variability, and environmental change.

What if these pulses aren’t truly saturating? A study by Karageorgou et al. (2007), published in the Journal of Plant Physiology , reveals that in leaves adapted to high levels of natural light, even the highest SP intensities afforded by commercial instruments (up to 13,000 μmol m⁻² s⁻¹) may fail to close all PSII centers. This can result in systematic underestimation of PSII yield and ETR, especially under the very conditions where accurate measurements matter most.

The saturating pulse method assumes that a pulse of light, typically several times brighter than full sunlight, will close all PSII reaction centers, allowing for the measurement of maximal fluorescence (Fm′). From Fm′, researchers derive the effective PSII yield and, by extension, the linear electron transport rate (ETR). These values are foundational for constructing light response curves, assessing photosynthetic efficiency, and diagnosing stress in plants.

However, Karageorgou and colleagues demonstrated that at high actinic light intensities (e.g., 1800 μmol m⁻² s⁻¹), even the highest SP intensities may not be sufficient to fully saturate PSII. In leaves of Nerium oleander , Euphorbia helioscopia , and Melissa officinalis , the apparent PSII yield dropped by up to 25% when SP intensity was reduced from its maximum to half-maximum, and this underestimation worsened with increasing actinic light. For N. oleander , even the highest affordable SP level (13,000 μmol m⁻² s⁻¹) failed to achieve true saturation at the highest actinic irradiances.

This phenomenon has real consequences. ETR versus light curves can become distorted, leading to mis-interpretations of photosynthetic performance. In some cases, the underestimation of ETR at high light may even mimic the appearance of downregulation, masking the true capacity of the plant’s photosynthetic machinery.

The study doesn’t pinpoint a single mechanistic cause, but it highlights a critical limitation of the PAM (pulse-amplitude modulation) technique: the measuring light probes only a peripheral, shallow layer of chloroplasts. At high actinic light, the steepness of gradients in PSII closure versus leaf depth may change, making it harder for the SP to fully close all PSII centers in the observed layer. Additionally, ferredoxin-NADP⁺ reductase (FNR), an enzyme on the acceptor side of Photosystem I (PSI), plays a pivotal role. When FNR is activated (as it is in light-adapted leaves), electrons flow out of PSI, preventing the full reduction of the plastoquinone (PQ) pool, even under saturating light. This means that not all PSII reaction centers can be "closed" by a saturating pulse, as some remain partially oxidized due to the outflow of electrons from PSI.

The se findings underscore the need for rigorous validation of SP saturation , particularly when working with leaves adapted to high light.

For further details, we encourage readers to explore the original publication:

Karageorgou, P., Tziortzis, I., Manetas, Y., (2007). Are saturating pulses indeed saturating? Evidence for considerable PSII yield underestimation in leaves adapted to high levels of natural light. Journal of Plant Physiology 164, 1331–1336. https://doi.org/10.1016/j.jplph.2006.07.015

Camera technology

Thu, 07 May 2026 14:32:17 GMT

Many of our standalone camera systems are used in plant research to non-destructively provide detailed insights into plant performance. The most popular measurements are those of photosynthetic efficiency. In combination with other measurements like chlorophyll content and morphological traits (see our website or related blogs), these parameters can help answer difficult research questions.

The PlantExplorer XS, PRO, and MAX systems have dedicated measurement chambers. This ensures minimal interreference from external factors and allows for proper dark adaptation, which is essential for measurements of chlorophyll fluorescence.

In contrast, systems like the PRO FIELD operate in an open environment. The PRO FIELD is frequently integrated into gantry systems for large-scale field experiments, where the camera moves over the plants to capture data across extensive growing areas (see images below).

But what makes a good image?

For plant research, it goes beyond aesthetics. Image quality is not only about sharpness, but also about factors like signal-to-noise ratio, dynamic range, and optical accuracy. It is a balance between light, sensor performance, and the optical setup of the system.

Image quality directly influences signal reliability, biological interpretation and ultimately the conclusions you can draw from the data.

In this blog I will dive deeper into the PhenoVation systems. I’ll cover the basics of camera technology, explain key differences in the system’s camera specifications, and go over how to set up the system for a measurement.

We’ll start with the basics.

Camera sensor

Of course, to make an image you'll obviously need a camera and a lens.

Inside the camera is the sensor (see image below) that is responsible for computing the digital image by capturing light (photons).

The sensor determines the number of pixels in the frame, but this alone does not define image quality. This is a common misconception in photography.

A great example is that modern smartphones are often marketed with extremely high megapixel counts, such as the 200 MP sensor in Samsung's latest Galaxy models. Yet, an image from a 12-24 MP DSLR or mirrorless camera is far superior in terms of image quality.

How is that possible?

The main reason is the size of the sensor. DSLR or mirrorless cameras have much larger sensors; approximately 864 mm ² instead of 70 mm ² , which is more than 12 times the surface area.

So, even with fewer pixels, each pixel is significantly larger. Larger pixels can capture more photons per exposure. More photons means more signal, and more signal means a better signal-to-noise ratio. And also a wider dynamic range.

The latter is crucial for the images that are captured with the PlantExplorer systems, since dynamic range is the ability to handle weak and strong signals at the same time. As we need to measure minimal and maximal chlorophyll fluorescence (see blogs on PAM or OJIP) to determine things like photosynthetic efficiency, we need a wide dynamic range. Minimal fluorescence is only a tiny fraction of the fluorescent light.

The PlantExplorer systems all contain the same camera sensor (aside from the CF2GO, which requires a high-speed camera). A table with the resolution of the different PlantExplorer systems is given below.

* A fast camera with a high framerate that can measure full OJIP images at a resolution of 720x500 pixels, can also be fitted in the PlantExplorer systems. The imaging area at this framerate decreases.
** This image area is measured 60 cm away from the lens

Lenses

Aside from the sensor, a lens is required to guide and focus the light into the camera. Lens quality plays a major role in determining how much light reaches the camera’s sensor, and how accurately that light is projected. The lens controls field of view, sharpness, distortion, and depth of field of the image.

With each PlantExplorer system, a range of different lenses can be used on the cameras (see table below). These lenses all fit on the same C-mount that is on the camera. The differences between these lenses are the focal distance.

The focal distance (usually given in mm) refers to the distance between an optical glass and the point where all light rays meet on the sensor of a camera. However, in today’s lens technology, this does not simply mean the length of the lens, since light is transmitted through many different optical glasses. The focal distance of a lens has a significant effect on the image, since it controls the field of view and the magnification (Figure 1 and figure 2).

Choosing the right focal distance for your study is important for the amount of detail required.

Lower magnification (e.g. 16, 25 mm): larger area, allowing better visuals for overall crop uniformity or detecting early canopy stress.
Higher magnification (e.g. 50, 75 mm): smaller area, but more detail. For instance to identify leaf stress patterns from pests or diseases.

The lens also controls the amount of light coming through the opening, which is called the aperture (see image below). The aperture is indicated by f-numbers (e.g. f/1.8, f/8). A low f-number means a large opening and lots of light, while a high f-number means a small opening with little light.

This opening of the aperture also controls the depth of field (the range of distance of the image that is in focus, sharp). A large opening (f/1.8) creates a shallow depth of field (blurred background), while a small opening (f/8, f/11, f/16) ensures that both the foreground and background are sharp (e.g. high f-numbers are needed for large distances or wide images). It is a balance between light and sharpness.

For plant imaging, we need the whole plant in focus, but we don’t need to image a great distance landscape, so we can choose slightly lower f-numbers. Therefore, the PlantExplorer systems use f/4.0, a number where the whole image of a plant is sharp and uniform, and also where the maximum optical performance of the lens lies (least distortion and vignetting).

Same sensor, different resolution

All PlantExplorer systems use the same camera sensor. However, you may have noticed that the PlantExplorer XS has a lower pixel count than other systems in the first table. Why is that?

It has to do with the size of the measurement chamber. Because the chamber of the PlantExplorer XS is relatively small, the system uses an 8 mm lens. This short focal length allows us to properly focus on taller plants within the limited height of the chamber. The trade-off, however, is that an 8 mm lens has a larger field of view, meaning it captures more area than needed, including a lot of the chamber walls.

Why not use a lens with a smaller field of view? This is due to the depth of field. Using a lens with a longer focal length requires a larger working distance to keep the entire plant in focus, which simply isn’t available within the compact measurement chamber. As with many imaging setups, this becomes a balance between field of view, depth of field, and physical space.

For this reason, the default configuration of the PlantExplorer XS uses an 8 mm lens, and the sensor is cropped to a square region to exclude the chamber walls from the final image (Figure 3). Cropping the sensor reduces the number of pixels.

Monochrome vs color pixel

The PlantExplorer imaging systems capture separate full-frame monochrome images to construct color images, giving pixel-to-pixel accuracy of the signal values. This is different than typical RGB cameras. How?

In essence, all camera sensors are colorblind (i.e. monochrome), meaning they only measure light intensity. A color image is created by separating the spectral bands of incoming light. This can be done by the use of specialized filters, using multiple sensors, or using stacked detection layers.

Nowadays, almost all modern digital (RGB) cameras are one-shot cameras, meaning they capture a color image in a single exposure. However, this is not as straightforward as it seems, because each pixel can only capture one color , either red, green or blue. But a full color image requires all three.

Therefore, most cameras use a Bayer filter (or Bayer pattern) as color filter array, in which each pixel captures only one color channel (Figure 4). Typically, this pattern consists of 50% green, 25% red, 25% blue, because green has the highest quantum efficiency and the human eye is most sensitive to green.

To reconstruct the full RGB image, these cameras rely on mathematical image processing techniques, such as debayering (or demosaicing), which uses algorithms to estimate (i.e. interpolate) the missing color information for each pixel based on neighbouring pixels (Figure 5).

However, by estimating values instead of measuring, it reduces the color resolution and true color accuracy of the image (i.e. more calculation errors due to interpolation) (see table below).

In the cameras of PhenoVation, as well as other high-end imaging systems (for example used in astronomy), separate red, green and blue images are captured that are pixel-to-pixel accurate by using multiple filters. For this, a filter wheel is used that rotates between the different frames to another filter to capture another spectral band (see image below).

For each spectral band, all pixels yield a specific amount of signal in that particular wavelength band. These separate images are stacked, creating a true pixel-to-pixel RGB image. As each pixel contains actual measured information, the sensitivity to light also increases (e.g. Bayer filters block out 2/3 of the signal). This results in more reliable quantitative measurements, which is particularly important for subtle spectral differences.

In imaging, every approach comes with trade-offs. The table below compares the differences between standard RGB cameras and separate-channel imaging systems.

Pixel binning

The PlantExplorer systems offer two sensor settings: 1×1 and 2×2 . This is pixel binning.

Pixel binning is a technique that can be used to improve signal yield if needed. The trade-off, however, is that the resolution decreases. The 1x1 binning setting offers the standard highest resolution, 12.3 Mp (or 5 Mp in the PlantExplorer XS). Setting the binning option to 2x2 decreases that resolution to 3 Mp, (1.3 Mp for XS). What happens in the camera?

With 2×2 pixel binning , four connected pixels are combined into a single pixel (see image below). The signal from these four pixels is summed, resulting in a stronger overall signal and, consequently, an improved signal-to-noise ratio. The trade-off is that fewer pixels are used to construct the image, leading to a lower spatial resolution.

Pixel binning can be particularly useful when working with plants that exhibit low fluorescence signals, where boosting signal strength is more important than achieving maximum resolution. In practice, the impact on overall image quality is often minimal. A good example is shown in the two images below, where maximum fluorescence (Fm) images appear nearly identical at first glance. Only when zooming in does the trade-off between increased signal and reduced spatial resolution become noticeable.

Focusing

From the PlantExplorer PRO onwards, image focusing is handled through the controller software using a motorized focusing system.

In contrast, the PlantExplorer XS uses a manual focus wheel located on the lens.

By enabling the ‘Live On’ option in the controller software, you can view a live video of the plant and manually adjust the focus to the correct distance. Note that this should not be done on a dark-adapted plant where you want to measure chlorophyll fluorescence, as the live view requires illumination and will disrupt dark adaptation.

After choosing the best lens for your system, setting the required pixel binning, and focusing the plant, you can start measuring, confident that image quality and consistency are not limiting factors.

Anthocyanins Uncovered: A Spectral Method for Stress and Senescence Detection in Plants

Thu, 30 Apr 2026 07:17:14 GMT

Anthocyanins (Anth) are water-soluble vacuolar pigments in higher plants, responsible for red coloration in leaves and often induced by environmental stresses such as UV radiation, drought, or nutrient deficiencies. Their accumulation serves as a marker for leaf stress and senescence, making their quantitative assessment valuable for understanding plant responses to environmental challenges. However, traditional methods for Anth estimation previously relied on destructive chemical extraction, which was time-consuming and impractical for large-scale or real-time studies. In their 2001 study, Gitelson et al. addressed this limitation by developing a nondestructive, reflectance-based method to estimate Anthocyanin content in intact leaves.

Gitelson et al. analyzed the absorption and reflectance spectra of leaves from four plant species; Acer platanoides (maple), Cotoneaster alaunica (cotoneaster), Cornus alba (dogwood), and Pelargonium zonale (pelargonium), across a wide range of pigment compositions. By comparing Anth-containing leaves to Anth-free leaves with matched chlorophyll (Chl) content, they identified a distinct absorption peak for Anth at 550 nm. This peak was linearly correlated with Anth content, establishing a direct relationship between spectral behavior and pigment concentration. However, the challenge lay in the spectral overlap between Anth, Chl, and carotenoids (Car) in the green region (500–600 nm), where Chl absorption is particularly strong. To estimate Anth accurately, it was necessary to account for Chl’s contribution to reflectance in this spectral range.

To isolate Anth’s spectral signal, Gitelson et al. developed the Anthocyanin Reflectance Index (ARI), defined as:

ARI = (R₅₅₀)⁻¹ – (R₇₀₀)⁻¹

where R₅₅₀ and R₇₀₀ are the inverse reflectances at 550 nm and 700 nm, respectively. The rationale behind this index is rooted in the optical properties of leaves:

At 700 nm, reflectance is minimally affected by Anth but strongly influenced by Chl. In Anth-free leaves, R₅₅₀ and R₇₀₀ are nearly equal, and their inverse values correlate closely with Chl content.
In Anth-containing leaves, Anth absorption reduces R₅₅₀ , causing the ARI to increase proportionally with Anth content. By subtracting the Chl-dominated signal at 700 nm, the ARI effectively isolates Anth’s contribution.

This method was validated across multiple species and pigment compositions, demonstrating a high linear correlation (R² = 0.94) between ARI and analytically measured Anth content. The standard error of estimation was ≤ 3 nmol/cm² for Anth levels between 0.3 and 25 nmol/cm². For higher Anth concentrations (>25 nmol/cm²), where the ARI’s sensitivity decreases, alternative indices such as (R₅₅₀)⁻¹ or R_NIR/R₅₅₀ (where R_NIR is reflectance in the near-infrared) were proposed, achieving a standard error of <3.9 nmol/cm².

Gitelson et al. compared the ARI to the red/green ratio (Gamon & Surfus, 1999), a previously proposed method for Anth estimation. The red/green ratio, however, is influenced by Chl, Car, and Anth simultaneously, leading to a weaker correlation (R² = 0.55) and a higher standard error (10.2 nmol/cm²). In contrast, the ARI demonstrated 2.5× greater accuracy, making it a superior method for Anth estimation in intact leaves.

For further details, we encourage readers to explore the original publication:

Gitelson, A.A., Merzlyak, M.N., Chivkunova, O.B., (2001). Optical properties and nondestructive estimation of anthocyanin content in plant leaves. Photochemistry and Photobiology,74(1), 38-45. doi: 10.1562/0031-8655(2001)074<0038:opaneo>2.0.co;2 .

The Power of the Red Edge: Using Gitelson’s Methods for Chlorophyll Measurement

Wed, 15 Apr 2026 08:41:45 GMT

According to Gitelson et al. (2003), the most accurate way to estimate chlorophyll content from reflectance measurements is to use the following algorithm:

Chlorophyll Index=(R _NIR /R _λ )−1

where:

R _NIR is the reflectance in the near-infrared (NIR) range (e.g., 750–800 nm).

R _λ is the reflectance in the green (520–585 nm) or red edge (695–740 nm) regions.

Gitelson et al. (2003) provide three key reasons for the superior accuracy of this approach:

1. Linear Relationship with Chlorophyll Content

The authors found that reciprocal reflectance (R _λ ) ⁻¹ in the green (520–585 nm) and red edge (695–740 nm) regions is linearly proportional to total chlorophyll content across a wide range of species and chlorophyll concentrations (1–830 µmol/m²).

In the blue (400–500 nm) and red (600–680 nm) regions, the relationship is non-linear and saturates at higher chlorophyll levels (>150 µmol/m²), making these regions less reliable for accurate estimation.

By subtracting the reciprocal reflectance in the NIR range (R _NIR ) ⁻¹ from (R _λ ) ⁻¹ , the authors created an index:

[(R _λ ) ⁻¹ −(R _NIR ) ⁻¹ ]

This subtraction eliminates the intercept (background noise) and makes the index linearly proportional to chlorophyll content across the entire range (1–830 µmol/m²).

2. Correction for Leaf Structure Variations

Impact of Leaf Structure: Leaf thickness, density, and internal scattering can vary significantly between species and even within the same plant. These structural differences affect reflectance, especially in the NIR range, where light scattering is dominant.

To account for structural variations, Gitelson et al. multiplied the index by R _NIR :

[(R _λ ) ⁻¹ −(R _NIR ) ⁻¹ ]⋅R _NIR =(R ^λ /R _NIR )−1

This adjustment reduces sensitivity to leaf thickness and density, making the algorithm more robust across different species and leaf structures.

3. Minimal Sensitivity to Pigment Composition

The slope of the relationship between (R _λ ) ⁻¹ and chlorophyll content is consistent in the green and red edge regions (520–585 nm and 695–740 nm) across different species. In contrast, the slopes in the blue and red regions vary widely, leading to less accurate estimates.

The coefficient of variation for the slope of the relationship (R _λ ) ⁻¹ vs. chlorophyll is minimal (<10%) in the green and red edge regions, ensuring high accuracy regardless of species or pigment composition.

The algorithm was validated using independent datasets (maple and beech leaves) and achieved an RMSE of less than 49 µmol/m² for chlorophyll estimation, which is significantly lower than other tested indices (e.g., R ₈₀₀ /R ₆₈₀ or (R ₈₀₀ −R ₆₈₀ )/(R ₈₀₀ +R ₆₈₀ ) ).

Gitelson et al. compared their algorithm with several previously developed indices (e.g., Blackburn 1998, Datt 1998) and found that their approach provided the lowest RMSE and the most linear relationship with chlorophyll content.

For further details, we encourage readers to explore the original publication:

Gitelson, A.A., Gritz †, Y., Merzlyak, M.N., (2003) . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves.

Journal of Plant Physiology 160, 271–282. https://doi.org/10.1078/0176-1617-00887

A Sustainable Solution for Rice Blast Control and Magnesium-Deficient Photosynthesis

Thu, 09 Apr 2026 12:47:50 GMT

Rice blast, caused by the fungal pathogen Magnaporthe oryzae, is a major threat to global rice production, capable of reducing yields by up to 50%. Conventional chemical fungicides, while effective, face increasing challenges due to pathogen resistance and environmental toxicity. Meanwhile, magnesium (Mg) deficiency—a widespread issue in intensive agriculture—compromises plant immunity and photosynthetic efficiency. Addressing both disease pressure and nutrient deficiency requires innovative, sustainable solutions. A recent study by Zhang et al. (2026), published in ACS Nano, introduces magnesium-doped zeolitic imidazolate framework-8 nanoparticles (Mg-ZIF-8 NPs)—a dual-functional nanomaterial designed to suppress rice blast and restore magnesium-deficient photosynthesis. This breakthrough offers a promising alternative to traditional agrochemicals, combining antifungal activity with nutrient supplementation.

The nanoparticles work by disrupting fungal cell membranes, inducing oxidative stress, and inhibiting the formation of infection structures in M. oryzae. At the same time, they replenish magnesium in rice leaves, restoring photosynthetic function and activating plant defense pathways. The study demonstrates that Mg-ZIF-8 NPs not only reduce disease severity but also improve rice growth under magnesium-deficient conditions, as evidenced by increased chlorophyll content, photochemical efficiency, and vegetative indices.

What makes this innovation particularly compelling is its systemic mobility within the plant. Mg-ZIF-8 NPs can be absorbed through both foliar and root application, ensuring targeted delivery to infection sites while minimizing environmental exposure. Advanced imaging tools, such as chlorophyll fluorescence imaging and multispectral analysis, were used to visualize the nanoparticles’ effects on rice physiology, confirming their ability to restore photosynthetic health and reduce stress responses. Biosafety assessments further support the potential of Mg-ZIF-8 NPs as a green nanofungicide. Tests on rice seeds, zebrafish, and earthworms revealed minimal toxicity, aligning with the principles of sustainable agriculture. The nanoparticles’ biodegradability and low ecological footprint make them an attractive option for modern farming systems seeking to reduce chemical inputs.

The study also highlights a paradigm shift in crop protection: the development of multifunctional nanomaterials that address both disease control and nutrient management. As climate change intensifies the pressure on global food systems, such innovations will be critical for optimizing resource use, reducing yield losses, and sustaining productivity under stress. The dual-functional nature of Mg-ZIF-8 NPs not only offers a powerful tool for rice blast management but also sets a precedent for the future of sustainable agriculture—where smart, eco-friendly solutions drive resilience in the face of adversity.

For further details, we encourage readers to explore the original publication:

Zhang, D., et al. (2026). Dual-Functional Magnesium-Doped Zeolitic Imidazolate Framework-8 Nanoparticles for Rice Blast Control and Restoration of Magnesium-Deficient Photosynthesis. ACS Nano 20, 2269–2286. https://doi.org/10.1021/acsnano.5c17850

Chlorophyll fluorescence measuring methods: OJIP, part 2

Mon, 30 Mar 2026 14:18:32 GMT

In part 1, I explained why the fluorescence of a leaf rises in steps (O, J, I and P) when you switch on a strong light. That curve is nice, but a curve is hard to compare. With 200 plants you cannot observe 200 curves next to each other and see which plants are stressed.

In this part I show how we turn the curve into numbers. This is called the JIP test. The numbers describe what happens with the light energy that a leaf absorbs, so they tell you where in the photosynthetic machinery things go well, and where they do not.

In short

The JIP test reads the fluorescence at a few fixed moments of the curve (O, J, I and P, and some points in between) and puts them in formulas. Every number has a physiological meaning.
The numbers follow the energy through the leaf: how much light is absorbed, how much of it reaches each step, and how much is lost as heat.
The performance index (PI) combines several of these steps in one number. It is one of the most sensitive OJIP parameters.
Under stress many parameters change, but whether they go up or down depends on the species, the type of stress and how severe it is. So look at several parameters together. Spider plots and leaf models help with that.

What happens to light energy in a leaf?

The JIP parameters follow the path of light energy, from the moment a photon is absorbed. The energy can be used to drive photosynthesis, or it is lost as heat or as fluorescence. At every step along the way, some energy is used and some is lost.

We group the parameters in three types:

Energy fluxes : how much energy flows through each step.
Quantum yields : what fraction of all the absorbed energy reaches a certain step.
Efficiencies : what fraction of the energy that reached one step continues to the next.

Energy fluxes: how much goes where?

A flux is an amount of energy flowing per second. The JIP test describes five of them:

Absorption (ABS). Photons are caught by the antenna pigments. This creates excited chlorophyll.
Trapping (TR). The energy of the excited chlorophyll is passed on to the reaction centre. There it starts the electron transport: QA takes up an electron.
Electron transport (ET). Electrons that move on beyond QA, into the rest of the chain.
Reduction of the end acceptors (RE). Electrons that arrive at the end of the chain, on the photosystem I side. (Scientists call taking up electrons "reduction".)
Dissipation (DI). Energy that is lost as heat or as fluorescence. Most of it is heat; the fluorescence we measure is only a small part.

Yields and efficiencies: how much is used?

Fluxes tell you how much energy flows. Often it is more useful to know which fraction makes it to the next step. There are two kinds of fractions, and they are easy to mix up:

A quantum yield (φ) counts from the start. It is the fraction of all absorbed photons that reaches a certain step.
An efficiency (ψ or δ) counts from the previous step. It is the fraction of the energy that arrived at one step and continues to the next.

An example, with made-up numbers. Say a leaf absorbs 100 photons.

80 of them are trapped and fill QA. That is a yield of 80 / 100 = 0.80 (φP₀).
Of those 80, 50 move an electron on beyond QA. Counted from the 80, that is an efficiency of 50 / 80 ≈ 0.63 (ψE₀). Counted from the original 100, it is a yield of 0.50 (φE₀).
Of those 50 electrons, 25 reach the end acceptors on the photosystem I side. Counted from the 50, that is an efficiency of 25 / 50 = 0.50 (δR₀). Counted from the original 100, it is a yield of 0.25 (φR₀).

These numbers are only here to show the idea. They are not measurements.

The five parameters in words:

φP₀, the maximum yield of the first step. How many of the absorbed photons fill QA. You will know it as Fv/Fm. In healthy leaves it is around 0.8.
φE₀, the yield of electron transport. How many of the absorbed photons move an electron on beyond QA, into QB and the PQ pool.
φR₀, the yield of the whole chain. How many of the absorbed photons end up as an electron at the end acceptors of photosystem I (FA/FB, ferredoxin or NADP⁺).
ψE₀, the efficiency beyond QA. Of the trapped energy, how much moves an electron on beyond QA.
δR₀, the efficiency towards the end acceptors. Of the electrons in the chain, how many reduce the end acceptors on the photosystem I side.

Together, these parameters show how well the photosynthetic machinery works. They are used, for example, to study how a compound acts on the plant, or what an environmental stress does.

Performance index: one number for the whole chain

Fv/Fm is not always sensitive enough to pick up early or small stress effects. The Performance Index (PI) was introduced for that reason. It puts several steps of photosynthesis in one number. Plant physiologists use it a lot to study the effect of biotic and abiotic stress. Breeders use it to screen many genotypes quickly, for example under drought or salt stress.

There are two versions.

• PI_ABS has three parts:

The density of active reaction centres: how many working reaction centres there are for the light that is absorbed.
The trapping efficiency: the chance that absorbed light fills QA.
The efficiency of electron transport beyond QA.

• PI_total has these three parts and adds a fourth:

4. The efficiency of electron transport to the final acceptors on the photosystem I side.

The parts are multiplied. A problem in any one step lowers PI, and small drops in several steps add up. That is why PI reacts more strongly than a single parameter. This is partly maths. For the trapping part, PI does not use Fv/Fm itself, but φP₀ / (1 − φP₀): the part that is used divided by the part that is lost. If Fv/Fm falls from 0.83 to 0.78, a drop of 6%, this part of PI falls from 4.9 to 3.5, a drop of 27%.

One limit: PI tells you that something changed, not where. To find where, you need the individual parameters. That is what the next section is about.

What does stress do to the parameters?

To see how the parameters are used in practice, we went through some literature. We looked at how five types of stress change the JIP parameters: salt, drought, heat, cold and high light. The studies are listed in the references at the end.

Several parameters change under stress in many studies. But the direction of the change is not always the same. A parameter can go up in one species or situation and down in another. The pattern depends on the species, sometimes even on the cultivar, and on how severe the stress is. So the same stress does not always give the same pattern.

An overview of all results is in a table:

How do you read all these numbers?

There are a lot of parameters, and that is difficult to visualize. Still, it is worth looking at all of them. Each one reflects another part of the photosynthetic machinery, and a change in one part may not show up in another. So with several parameters side by side you can see where a change or stress response takes place.

Spider plots

A spider plot shows many parameters in one picture. Each spoke is one parameter, and each treatment is a line. Usually the control is the reference, so the lines show how far each treatment moves away from it. This makes it easy to see patterns, to compare treatments, and to spot the parameters that stress has changed.

The leaf model

Some studies draw the energy fluxes in a leaf model (Figure 2). In this drawing the width of an arrow shows how large a flux is:

yellow: absorption
light blue: trapping
dark blue: electron transport
red: dissipation

The black dots in the central square are reaction centres that are inactive ("silent").

The leaf model shows the fluxes per cross section. That means: per piece of leaf that was measured. So it also includes how many reaction centres are active.

The model is useful because one picture sums up the state of the photosynthetic machinery. In the example above, two barley cultivars were compared in high light and in low light. The cultivar ‘Arabi Aswad’ (left) copes better with high light: it has a larger electron transport flux, a smaller dissipation flux and more active reaction centres. The cultivar ‘Arabi Abiad’ (right) is better adapted to low light.

The membrane model

The same fluxes can also be shown per reaction centre (RC). This is the membrane model (Figure 3).

The two views tell you different things. Per cross section shows what the leaf as a whole is doing. Per reaction centre shows what each working unit is doing. Together they show whether a change comes from fewer working units, or from units that work differently.

An example: what happens when a leaf is heated?

Strasser describes the effect of heating in both models. (This is a summary in plain words.)

Per reaction centre (membrane model):

Absorption per active reaction centre goes up. Some reaction centres have been switched off, so the active ones share the absorbed light.
Dissipation per active reaction centre goes up. The switched-off centres lose all their energy as heat, and this heat is counted against the active ones.
Electron transport per active reaction centre goes up. Heat speeds up the dark reactions, the enzyme reactions that use the electrons.

Per cross section (leaf model):

The density of active reaction centres (RC/CS) goes down, because reaction centres are switched off.
Electron transport per cross section goes down, for the same reason.
Dissipation per cross section goes up.
The absorbed energy per cross section (ABS/CS) goes down.

4. References

1. Strasser, R.J., Srivastava, A. And Govindjee (1995). Polyphasic chlorophyll a fluorescence transient in plants and cyanobacteria. Photochemistry and Photobiology 61: 32-42.

2. Strasser, R.J., Srivastava, A. And Tsimilli-Michael, M. (2000). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Ynus, M. (Ed.) Probing Photosynthesis: Mechanisms, Regulation and Adaptation. Taylor and Francis, London: 445-483.

3. Strasser, R.J., Tsimilli-Micael, M. and Srivastava, A. (2004). Analysis of the Chlorophyll a fluorescence transient. In: Papageorgiou, G.C. and Govindjee (Eds.) Chlorophyll a fluorescence: a signature of photosynthesis. Springer, Dordrecht: 321-362.

4. Lotfi, R., H. M. Kalaji, G. R. Valizadeh, E. Khalilvand Behrozyar, A. Hemati, P. Gharavi‑Kochebagh, and A. Ghassemi. 2018. Effects of humic acid on photosynthetic efficiency of rapeseed plants growing under different watering conditions. Photosynthetica 56 (3): 962–970. https://doi.org/10.1007/s11099-017-0745-9.

5. Zushi, K., S. Kajiwara, and N. Matsuzoe. 2012. Chlorophyll a fluorescence OJIP transient as a tool to characterize and evaluate response to heat and chilling stress in tomato leaf and fruit. Scientia Horticulturae 148 : 39–46. https://doi.org/10.1016/j.scienta.2012.09.022.

6. Kalaji, H. M., A. Rastogi, M. Živčák, M. Brestič, A. Daszkowska‑Golec, K. Sitko, K. Y. Alsharafa, R. Lotfi, P. Stypiński, I. A. Samborska, and M. D. Cetner. 2018. Prompt chlorophyll fluorescence as a tool for crop phenotyping: an example of barley landraces exposed to various abiotic stress factors. Photosynthetica 56 : 953–961. https://doi.org/10.1007/s11099-018-0766-z.

7. Chen, X., Y. Zhou, Y. Cong, P. Zhu, J. Xing, J. Cui, W. Xu, Q. Shi, M. Diao, and H.‑Y. Liu. 2021. Ascorbic acid‑induced photosynthetic adaptability of processing tomatoes to salt stress probed by fast OJIP fluorescence rise. Frontiers in Plant Science 12 : 594400. https://doi.org/10.3389/fpls.2021.594400.

9. Stefanov, D., V. Petkova, and I. D. Denev. 2011. Screening for heat tolerance in common bean (Phaseolus vulgaris L.) lines and cultivars using JIP‑test. Scientia Horticulturae 128 (1): 1–6. https://doi.org/10.1016/j.scienta.2010.12.003.

11. Jedmowski, C., and W. Brüggemann. 2015. Imaging of fast chlorophyll fluorescence induction curve (OJIP) parameters, applied in a screening study with wild barley (Hordeum spontaneum) genotypes under heat stress. Journal of Photochemistry and Photobiology B: Biology 151 : 153–160. https://doi.org/10.1016/j.jphotobiol.2015.07.020.

13. Kalaji, H. M., A. Oukarroum, V. Alexandrov, M. Kouzmanova, M. Brestic, M. Zivcak, I. A. Samborska, M. D. Cetner, S. I. Allakhverdiev, and V. Goltsev. 2014. Identification of nutrient deficiency in maize and tomato plants by in vivo chlorophyll a fluorescence measurements. Plant Physiology and Biochemistry 81 : 16–25. https://doi.org/10.1016/j.plaphy.2014.03.029.

14. Mehta, P., S. I. Allakhverdiev, and A. Jajoo. 2010. Characterization of photosystem II heterogeneity in response to high salt stress in wheat leaves (Triticum aestivum). Photosynthesis Research 105 (3): 249–255. https://doi.org/10.1007/s11120-010-9588-y.

15. Jedmowski, C., A. Ashoub, and W. Brüggemann. 2013. Reactions of Egyptian landraces of Hordeum vulgare and Sorghum bicolor to drought stress, evaluated by the OJIP fluorescence transient analysis. Acta Physiologiae Plantarum 35 : 345–354. https://doi.org/10.1007/s11738-012-1077-9.

16. Ren, J., P. Guo, X. Zhao, X. Ma, X. Ai, J. Wang, H. Zou, and H. Yu. 2025. Differential photosynthetic responses to drought stress in peanut varieties: insights from transcriptome profiling and JIP‑Test analysis. BMC Plant Biology 25 : 957. https://doi.org/10.1186/s12870-025-06984-y.

17. Mihaljević, I., M. Viljevac Vuletić, V. Tomaš, Z. Zdunić, D. Vuković, and K. Dugalić. 2024. Assessment of photosynthetic capacity of two blackberry cultivars subjected to salt stress by the JIP fluorescence test. Journal of Berry Research 14 (1): 1–13. https://doi.org/10.3233/JBR-230026.

18. Rastogi, A., M. Kovář, X. He, M. Živčák, S. Kataria, H. M. Kalaji, M. Skalicky, U. F. Ibrahimova, S. Hussain, S. Mbarki, and M. Brestic. 2020. JIP‑test as a tool to identify salinity tolerance in sweet sorghum genotypes. Photosynthetica 58 (Special Issue): 518–528. https://doi.org/10.32615/ps.2019.169.

19. Ranjbarfordoei, A., R. Samson, and P. Van Damme. 2006. Chlorophyll fluorescence performance of sweet almond (Prunus dulcis (Miller) D. Webb) in response to salinity stress induced by NaCl. Photosynthetica 44( 4): 513–522. https://doi.org/10.1007/s11099-006-0064-z.

20. Jafarinia, M., and M. Shariati. 2012. Effects of salt stress on photosystem II of canola plant (Brassica napus L.) probed by chlorophyll a fluorescence measurements. Iranian Journal of Science and Technology (Sciences) 36 (1): 71–76. https://doi.org/10.22099/ijsts.2012.2058.

21. Zushi, K., and N. Matsuzoe. 2017. Using of chlorophyll a fluorescence OJIP transients for sensing salt stress in the leaves and fruits of tomato. Scientia Horticulturae 219 : 216–221. https://doi.org/10.1016/j.scienta.2017.03.016.

22. Giorio, P., and M. H. Sellami. 2021. Polyphasic OKJIP chlorophyll a fluorescence transient in a landrace and a commercial cultivar of sweet pepper (Capsicum annuum L.) under long‑term salt stress. Plants 10 (5): 887. https://doi.org/10.3390/plants10050887.

23. Akhter, M. S., S. Noreen, S. Mahmood, H.‑u‑R. Athar, M. Ashraf, A. A. Alsahli, and P. Ahmad. 2021. Influence of salinity stress on PSII in barley (Hordeum vulgare L.) genotypes, probed by chlorophyll‑a fluorescence. Journal of King Saud University – Science 33 (1): 101239. https://doi.org/10.1016/j.jksus.2020.101239.

24. Zhang, T., H. Gong, X. Wen, and C. Lu. 2010. Salt stress induces a decrease in excitation energy transfer from phycobilisomes to photosystem II but an increase to photosystem I in the cyanobacterium Spirulina platensis. Journal of Plant Physiology 167 (12): 951–958. https://doi.org/10.1016/j.jplph.2009.12.020.

25. Weng, H., M. Wu, X. Li, L. Wu, J. Li, T. O. Atoba, J. Zhao, R. Y. Wu, and D. Ye. 2023. High‑throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy. Plant Science 330 : 111660. https://doi.org/10.1016/j.plantsci.2023.111660.

26. Chen, X., X. Liu, Y. Cong, Y. Jiang, J. Zhang, Q. Yang, and H. Liu. 2025. Melatonin alleviates photosynthetic injury in tomato seedlings subjected to salt stress via OJIP chlorophyll fluorescence kinetics. Plants 14 (5): 824. https://doi.org/10.3390/plants14050824.

27 . Antunović Dunić, J., Mlinarić, S., Pavlović, I., Lepeduš, H., and Salopek‑Sondi, B. 2023. Comparative analysis of primary photosynthetic reactions assessed by OJIP kinetics in three Brassica crops after drought and recovery. Applied Sciences 13(5): 3078. https://doi.org/10.3390/app13053078.

28 . Zhang, R. H., Zhang, X. H., Camberato, J. J., and Xue, J. Q. 2015. Photosynthetic performance of maize hybrids to drought stress. Russian Journal of Plant Physiology 62: 788–796. https://doi.org/10.1134/S1021443715060187.

29 . Meng, L.‑L., Song, J.‑F., Wen, J., Zhang, J., and Wei, J.‑H. 2016. Effects of drought stress on fluorescence characteristics of photosystem II in leaves of Plectranthus scutellarioides. Photosynthetica 54(3): 414–421. https://doi.org/10.1007/s11099‑016‑0191‑0.

30 . Zhou, R., Kan, X., Chen, J., Hua, H., Li, Y., Ren, J., Feng, K., et al. 2019. Drought‑induced changes in photosynthetic electron transport in maize probed by prompt fluorescence, delayed fluorescence, P700 and cyclic electron flow signals. Environmental and Experimental Botany 158: 51–62. https://doi.org/10.1016/j.envexpbot.2018.11.005.

31 . Liu, J., Li, H. J., Guo, Y. Y., Wang, G. X., Zhang, H. J., Zhang, R. H., and Xu, W. H. 2018. Effects of drought stress on the photosynthesis in maize. Russian Journal of Plant Physiology 65: 849.

32 . Zhang, K., Chen, B‑h., Hao, Y., Yang, R., and Wang, Y‑a. 2018. Effects of short‑term heat stress on PSII and subsequent recovery for senescent leaves of Vitis vinifera L. cv. Red Globe. Journal of Integrative Agriculture 17(12): 2683–2693. https://doi.org/10.1016/S2095‑3119(18)62143‑4.

33 . Zhang, L., Hu, T., Amombo, E., Wang, G., Xie, Y., and Fu, J. 2017. The alleviation of heat damage to photosystem II and enzymatic antioxidants by exogenous spermidine in tall fescue. Frontiers in Plant Science 8: 1747. https://doi.org/10.3389/fpls.2017.01747.

34 . Fan, Q., and Jespersen, D. 2023. Assessing heat tolerance in creeping bentgrass lines based on physiological responses. Plants 12(1): 41. https://doi.org/10.3390/plants12010041.

35 . Tan, W., Meng, Q‑W., Brestič, M., Olsovská, K., and Yang, X. 2011. Photosynthesis is improved by exogenous calcium in heat‑stressed tobacco plants. Journal of Plant Physiology 168: 2063–2071. https://doi.org/10.1016/j.jplph.2011.06.009.

36 . Arslan, Ö. 2023. The role of heat acclimation in thermotolerance of chickpea cultivars: Changes in photochemical and biochemical responses. Life 13(1): 233. https://doi.org/10.3390/life13010233.

37 . Mihaljević, I., Viljevac Vuletić, M., Tomaš, V., Vuković, D., and Zdunić, Z. 2025. Characterization of heat tolerance in two apple rootstocks using chlorophyll fluorescence as a screening method. Agronomy 15(6): 1442. https://doi.org/10.3390/agronomy15061442.

38 . Kalaji, H. M., Carpentier, R., Allakhverdiev, S. I., and Bosa, K. 2012. Fluorescence parameters as early indicators of light stress in barley. Journal of Photochemistry and Photobiology B: Biology 112: 1–6. https://doi.org/10.1016/j.jphotobiol.2012.03.009.

39 . Lee, J. H., Cabahug, R. A. M., You, N. H., and Nam, S. Y. 2021. Chlorophyll fluorescence and growth evaluation of ornamental foliage plants in response to light intensity levels under continuous lighting conditions. Flower Research Journal 29(4): 320–322. https://doi.org/10.11623/frj.2021.29.3.05.

40 . Bayat, L., Arab, M., Aliniaeifard, S., Seif, M., Lastochkina, O., and Li, T. 2018. Effects of growth under different light spectra on the subsequent high light tolerance in rose plants. AoB Plants 10(5): ply052. https://doi.org/10.1093/aobpla/ply052.

41 . Ceusters, N., Valcke, R., Frans, M., Claes, J. E., Van den Ende, W., and Ceusters, J. 2019. Performance Index and PSII connectivity under drought and contrasting light regimes in the CAM orchid Phalaenopsis. Frontiers in Plant Science 10: 1012. https://doi.org/10.3389/fpls.2019.01012.

42 . Faseela, P., and Puthur, J. T. 2016. Chlorophyll a fluorescence changes in response to short‑ and long‑term high‑light stress in rice seedlings. Photosynthetica 54(3): 549–558.

43 . Faseela, P., and Puthur, J. T. 2018. The imprints of the high light and UV‑B stresses in Oryza sativa L. ‘Kanchana’ seedlings are differentially modulated. Journal of Photochemistry and Photobiology B: Biology 178: 551–559. https://doi.org/10.1016/j.jphotobiol.2017.12.009.

44 . Mlinarić, S., Antunović Dunić, J., Skendrović Babojelić, M., Cesar, V., and Lepeduš, H. 2017. Differential accumulation of photosynthetic proteins regulates diurnal photochemical adjustments of PSII in common fig (Ficus carica L.) leaves. Journal of Plant Physiology 209: 1–10. https://doi.org/10.1016/j.jplph.2016.12.002.

45 . Lu, T., Yu, H., Li, Q., Chai, L., and Jiang, W. 2019. Improving plant growth and alleviating photosynthetic inhibition and oxidative stress from low‑light stress with exogenous GR24 in tomato (Solanum lycopersicum L.) seedlings. Frontiers in Plant Science 10: 490. https://doi.org/10.3389/fpls.2019.00490.

46 . Li, L., Li, X.‑Y., Xu, X.‑W., Lin, L.‑S., Zeng, F.‑J., and Chen, F.‑L. 2013.
Assimilative branches and leaves of the desert plant Alhagi sparsifolia Shap. possess a different adaptation mechanism to shade.
Plant Physiology and Biochemistry 74: 239–245. https://doi.org/10.1016/j.plaphy.2013.11.009.

47 . Liang, Y., Chen, H., Tang, M., Yang, P., and Shen, S. 2007. Responses of Jatropha curcas seedlings to cold stress: photosynthesis‑related proteins and chlorophyll fluorescence characteristics. Physiologia Plantarum 131(3): 508–517. https://doi.org/10.1111/j.1399‑3054.2007.00974.x.

48 . Huang, X., Shi, H., Hu, Z, Liu, A., Amombo, E., Chen, L., and Fu, J. 2017. ABA is involved in regulation of cold stress response in Bermudagrass. Frontiers in Plant Science 8: 1613. https://doi.org/10.3389/fpls.2017.01613.

49 . Mazur, M., Matoša Kočar, M., Jambrović, A., Sudarić, A., Volenik, M., Duvnjak, T., and Zdunić, Z. 2024. Crop‑specific responses to cold stress and priming: Insights from chlorophyll fluorescence and spectral reflectance analysis in maize and soybean. Plants 13(9): 1204. https://doi.org/10.3390/plants13091204.

50 . Fan, J., Hu, Z., Xie, Y., Chan, Z., Chen, K., Amombo, E., Chen, L., and Fu, J. 2015. Alleviation of cold damage to photosystem II and metabolisms by melatonin in Bermudagrass. Frontiers in Plant Science 6: 925. https://doi.org/10.3389/fpls.2015.00925.

51 . Li, J., Muneer, M. A., Sun, A., Guo, Q., Wang, Y., Huang, Z., Li, W., and Zheng, C. 2023. Magnesium application improves the morphology, nutrient uptake, photosynthetic traits, and quality of tobacco (Nicotiana tabacum L.) under cold stress. Frontiers in Plant Science 14: 1078128. https://doi.org/10.3389/fpls.2023.1078128.

52 . Sun, L., Li, X., Wang, Z., Sun, Z., Zhu, X., Liu, S., Song, F., Liu, F., and Wang, Y. 2018. Cold priming induced tolerance to subsequent low temperature stress is enhanced by melatonin application during recovery in wheat. Molecules 23(5): 1091. https://doi.org/10.3390/molecules23051091.

53 . Tsimilli-Michael, M. (2020). Revisiting JIP-test: An educative review on concepts, assumptions, approximations, definitions and terminology. PHotosynthetica 58: 275-292.

How Common Bean Leaves Cope with Moderate Drought

Mon, 30 Mar 2026 10:03:46 GMT

Drought stress is a major constraint on crop productivity, but how plants spatially regulate their physiological responses to water deficit remains poorly understood. While it’s often assumed that drought increases heterogeneity in leaf function, leading to “patchy” stomatal closure and photosynthetic activity—this study challenges that assumption. Using imaging-based phenotyping, the authors reveal that moderate drought in common bean (Phaseolus vulgaris) does not intensify within-leaf heterogeneity. Instead, it promotes a spatially coherent down-regulation of photosynthesis, photochemistry, and optical properties, maintaining functional integrity despite reduced assimilation.

This study combined gas exchange measurements , chlorophyll fluorescence imaging , and multispectral imaging to analyze the spatial variability of physiological and optical traits in common bean leaves under well-watered (control) and moderate drought conditions . It focused on three leaf positions— basal, middle, and apical —to assess how drought influences spatial patterns.

Under control conditions, common bean leaves exhibited subtle spatial gradients: lower photosynthetic activity, photochemical efficiency, and pigment-related indices at the apical region compared to basal and middle segments. However, drought reduced the magnitude of these traits—net photosynthesis, stomatal conductance, and electron transport rate all declined—but did not amplify positional differences. Instead, drought diminished spatial variability, leading to a more uniform down-regulation of leaf function.

Drought reduced stomatal size and increased stomatal density, but these changes were uniform across the leaf. Leaf relative water content (RWC) declined under drought but remained spatially consistent, indicating coordinated hydraulic responses. This suggests that common bean maintains synchronized stomatal regulation even under stress, preventing pa tchy closure.

Under well-watered conditions, PLSR models strongly predicted stomatal conductance using imaging-derived traits (e.g., Fq'/Fm', rETR, NDVI). However, under drought, predictive power collapsed, likely because stomatal conductance was uniformly low, and imaging traits primarily reflected photoprotective adjustments rather than dynamic stomatal behavior.

Drought increased non-photochemical quenching (NPQ) and altered reflectance in blue, red, and near-infrared wavelengths, indicating pigment and structural adjustments. Yet, these changes were spatially uniform, further supporting the idea of coordinated acclimation rather than patchy stress responses.

The study suggests that spatial coherence in leaf responses to drought may be an adaptive mechanism, preserving hydraulic and photosynthetic integrity. This challenges the traditional view of drought-induced patchiness and highlights the need to account for spatial stability in phenotyping studies.

As climate change intensifies, understanding the hidden rules of spatial stability will be key to building resilience. The future of stress physiology lies not just in measuring what plants lose under adversity, but in uncovering how they reorganize to endure it. This work is a call to look closer, measure smarter, and rethink how we define, and harness, plant adaptability in the face of environmental challenge.

For further details, we encourage readers to explore the original publication:

Lazarević, B., et al. (2026). Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging. BMC Plant Biology. DOI doi.org/10.1186/s12870-026-08233-2

Chlorophyll Fluorescence Imaging Quantifies Stress in Weeds Under Mechanical and Chemical Control

Mon, 26 Jan 2026 09:15:33 GMT

Weeds remain one of agriculture’s most persistent challenges, competing with crops for water, nutrients, and sunlight, often leading to significant yield losses. While chemical herbicides and mechanical weeding are the most common control methods, their precise effects on weed physiology, and how weeds respond to these stresses, have not been fully explored. A recent study by Quan et al. (2023) in Frontiers in Plant Science sheds light on this issue by using chlorophyll fluorescence imaging to monitor how weeds react to mechanical and chemical damage.

The researchers focused on two common weed species: Digitaria sanguinalis (large crabgrass) and Erigeron canadensis (Canadian fleabane). Using PhenoVation’s PlantExplorer KS, a mobile chlorophyll fluorescence imaging system, they tracked changes in key photosynthetic parameters, such as Fv/Fm (maximum quantum yield of PSII) and ETR (electron transport rate), to assess how different stress treatments affected weed health.

One of the most striking findings was the site-specific impact of chemical stress. When the herbicide glufosinate was applied to different parts of the weeds, the leaf underside (abaxial surface) showed the most severe damage, with a photosynthetic inhibition rate (R) of 75% after seven days. This suggests that herbicides penetrate more effectively through the leaf underside, possibly due to differences in cuticle structure or stomatal distribution. The study also noted that Erigeron canadensis, with its dense leaf trichomes, exhibited a slower response to chemical stress compared to Digitaria sanguinalis, highlighting the importance of species-specific responses in weed management.

Mechanical stress, simulated by scratching the leaves to mimic weeding machinery, resulted in a short-term decline in photosynthetic efficiency, followed by partial recovery within one to two days. The photosynthetic inhibition rate (R) for severe mechanical stress was 11%, significantly lower than that of chemical stress. Unlike chemical damage, which is often irreversible, mechanical stress triggered a transient physiological response, with parameters like Fv/Fm and ETR initially dropping but beginning to recover after 48 hours. However, severe mechanical damage—affecting more than 50% of the leaf area—led to more pronounced and lasting effects, indicating that the degree of injury plays a critical role in weed survival.

Fluorescence images of Fv/Fm parameters were captured daily during the experimental period, and the changes of Fv/Fm were monitored continuously. (A, B) shows the image changes of Fv/Fm in Erigeron canadensis with different levels of mechanical stress, (C, D) shows changes of Fv/Fm in Digitaria sanguinalis with different levels of mechanical stress.

The study’s most intriguing finding was the effect of combined mechanical and chemical stress. When weeds were subjected to both types of damage simultaneously, the photosynthetic inhibition rate (R) reached 71–73% after just three to four days—a level of damage comparable to seven days of chemical stress alone. This suggests that mechanical damage enhances herbicide efficacy by breaking the leaf cuticle, allowing chemicals to penetrate more deeply and act more rapidly. This synergistic effect underscores the potential of integrated weed management strategies that combine mechanical and chemical methods for more efficient control.

The implications of this research are significant for agricultural practices. By understanding how weeds respond to different types of stress, farmers can optimize their control strategies—whether through targeted herbicide application, mechanical weeding as a complementary tool, or species-specific approaches that account for variations in leaf morphology. Chlorophyll fluorescence imaging provides a real-time, non-destructive method for assessing weed stress, allowing for dynamic adjustments in weed management.

In conclusion, the study by Quan et al. (2023) highlights the value of chlorophyll fluorescence imaging in understanding and optimizing weed control. By revealing the hidden physiological responses of weeds to mechanical and chemical stresses, this research offers a scientific foundation for developing more effective, sustainable, and precise weed management practices.

For further details, we encourage readers to explore the original publication:

Quan, L., et al. (2023). Monitoring weed mechanical and chemical damage stress based on chlorophyll fluorescence imaging. Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1188981

Chlorophyll fluorescence measuring methods: OJIP, part 1

Wed, 17 Dec 2025 13:57:38 GMT

In short

In an OJIP measurement, a dark-adapted leaf gets one second of extremely bright light, and we follow how its fluorescence climbs.
The curve rises in steps, called O, J, I and P. Each step shows the flow of electrons in photosynthesis.
From the shape of the curve we calculate how well the photosynthetic machinery works. Part 2 explains those parameters.

Why look at fluorescence?

Chlorophyll fluorescence is a fast, non-destructive way to observe how efficiently a plant uses light. It is used to screen plants, for example when breeding a new cultivar or testing a new product. High-tech greenhouses use it as direct feedback from the plant to steer climate and lighting. Ecology, forestry and arable farming use it too, with drones and satellites (solar-induced fluorescence, SIF).

Our CF2GO and PlantExplorer systems measure chlorophyll fluorescence with two protocols: PAM and OJIP. Both protocols measure a minimum and a maximum fluorescence level. PAM measures just those two levels (see our PAM blog). OJIP follows the whole rise from minimum to maximum, in detail.

That rise was first described by Kautsky and Hirsch in 1931, and it is known in the literature as the Kautsky effect. It looks simple, but it carries a surprising amount of information about how a plant handles light energy. The model that we use was developed by Strasser and colleagues (Strasser et al., 1995). In this part, I explain the model. In part 2, I explain the parameters we calculate with it.

Why does fluorescence go up? The basic idea

A leaf that absorbs light can do three things with that energy (Figure 1):

use it for photosynthesis,
release it as heat,
release it as a tiny bit of red light: fluorescence.

When photosynthesis uses less of the energy, more of it is lost as heat and fluorescence. So fluorescence goes up whenever photosynthesis 'gets stuck' . That is what the OJIP curve shows: the moments when the electron flow of photosynthesis gets stuck, and where.

The electron transport chain in one minute

In photosystem II (PSII), light energy is used to push an electron from the reaction centre onto a chain of electron-carriers (Figure 2). The electron travels along the chain, like a car on a road with 'merging sections'. The 'sections' in the order that matter for OJIP are:

QA : the first carrier in PSII. It holds only one electron at a time.
QB : the second carrier. It needs two electrons before it moves on.
PQ pool : a stock of small carriers (plastoquinone) that float in the membrane.
Cytochrome b6f : a protein complex that takes the electrons from the PQ pool, but only at a limited speed.
Plastocyanin (PC) and photosystem I (PSI) : the next stations.
Ferredoxin, FNR and NADPH : the end of the road, where the electrons are finally put to use (ATP and NADPH)

When a 'section' is blocked, the light energy that arrives cannot get through and turns back. That "turning back" is what we see as extra fluorescence.

How the measurement works

A leaf that has been in the dark gets an extremely bright flash of light for one second. A camera (or photodiode) with a sensitive sensor and a filter records the weak fluorescence signal that comes from the plant.

The signal does not rise in a straight line. Most of the action happens in the first thousandths of a second, so we plot time on a logarithmic axis, where each step to the right is ten times later. Then the following pattern appears:

The fluorescence climbs in three steps, which Strasser called triphasic kinetics. It reaches a first plateau, J, after about 3 milliseconds, and a second plateau, I, after about 33 milliseconds. The last plateau, P, is the peak or maximum fluorescence, after roughly 300 to 800 milliseconds. The start of the curve is called O, for origin.

O: the start of the curve (Fo)

At the very start of the flash, all QA are still 'empty' (also called 'oxidized'). We call this state "open". Most of the absorbed light energy can be used for photosynthesis, and only a small fraction falls back as fluorescence. That small signal is the minimal fluorescence, Fo (the O stands for origin). It is measured within 50 microseconds from the onset of the flash.

In short: the road is free everywhere, so the fluorescence is at its lowest.

O to J: a queue forms at QA

When a photosystem absorbs light, the energy reaches the reaction centre within a few femtoseconds. The reaction centre sends the energy to an electron, which moves to QA, and water splitting then supplies a new electron in the reaction center to replace it.

Now QA is 'occupied' (also called 'reduced), which we call "closed". It cannot accept another electron until it has passed the first one on to QB. But absorbing light is much faster than passing electrons on. So during the flash, energy that arrives at a photosystem with a closed QA cannot be used for photosynthesis. It falls back and is released as fluorescence (remember the three fates of energy).

The more QA are closed, the more fluorescence we see. That is the rise from O to J.

In short: QA fills faster than it empties, so a queue forms at the first merging section and fluorescence rises.

J-plateau: QA empties as fast as it fills

After about 3 milliseconds, QA starts to pass its electrons to QB. That frees QA again, so light energy can be used for photosynthesis again and less of it is lost as fluorescence. For a short moment QA is emptied as fast as it is filled. The fluorescence stays level: this is the J plateau.

J to I: the PQ pool fills up

QB needs two electrons before it can move on. When it has them, it takes up two protons (H⁺) from the stroma, the fluid outside the thylakoid membrane, and becomes plastoquinol (PQH₂). PQH₂ leaves photosystem II and joins the PQ pool in the membrane. A new plastoquinone takes its place at QB.

Cytochrome b6f takes the electrons from the PQ pool, but only at a limited speed. That is a bottleneck. PQH₂ piles up in the pool, QB stays occupied, and QA cannot pass on its electrons. More and more QA stay closed, so more light energy turns back as fluorescence. That is the rise from J to I.

In short: electrons arrive in the PQ pool faster than cytochrome b6f can take them on, so the queue grows again.

I-plateau: electrons move on to photosystem I

After about 30 milliseconds, plastocyanin (PC) and photosystem I (PSI) start to take electrons from cytochrome b6f. PSI uses them, together with its own captured light energy, to make NADPH, a key product of the light reactions of photosynthesis.

With the electrons flowing on, there is room again. QA empties about as fast as it is filled, and the fluorescence stays level: this is the I plateau.

I to P: the end of the road is closed

But the flow does not keep up for long. After the previous period of darkness, the reactions behind photosystem I are still 'switched off'. This includes FNR, the enzyme that makes NADPH, and the enzymes of the Calvin cycle. They need light and some time to switch on, and how long differs between species. Under the extremely bright light of the saturation pulse, electrons pile up at this end of the chain.

Finally the whole chain is full. All electron carriers are occupied and cannot accept more electrons, and photosynthesis is completely blocked. No light energy can be used any more, and it is released as fluorescence. The fluorescence reaches its maximum, Fm: the P peak.

In short: the end of the road is still closed, so the whole road jams and fluorescence reaches its maximum.

What can we do with this?

During the one-second flash we can follow many processes in the plant. From the plateaus and the shape of the curve, we can calculate how well the photosynthetic machinery works. Strasser, his colleagues and other researchers developed more than 20 parameters for this. They range from the overall efficiency of the system to single processes, for example:

Light absorption and trapping: the size of the antenna, and how much energy reaches the reaction centres.
Reaction centres: how many there are per chlorophyll, how many are open, and how efficient each one is.
Electron transport: how many electrons move on per reaction centre, and how efficiently they reach the different carriers.
Energy conversion: how much energy is used for photochemistry and how much is lost.

Stress, such as drought, heat, salt, pests and diseases, changes how this system works, so it can show up in these measurements. The effect of a product on the plant can also be quantified.

In part 2 of the OJIP blog, I explain the parameters that we calculate from the OJIP rise, what they mean, and what stress does to them.

References

• Kautsky, H. and Hirsch, A. (1931). Neue Versuche zur Kohlensäureassimilation. Naturwissenschaften 19: 964.

• Strasser, R.J., Srivastava, A. And Govindjee (1995). Polyphasic chlorophyll a fluorescence transient in plants and cyanobacteria. Photochemistry and Photobiology 61: 32-42.

• Strasser, R.J., Srivastava, A. And Tsimilli-Michael, M. (2000). The fluorescence transient as a tool to characterize and screen photosynthetic samples. In: Ynus, M. (Ed.) Probing Photosynthesis: Mechanisms, Regulation and Adaptation. Taylor and Francis, London: 445-483.

• Strasser, R.J., Tsimilli-Micael, M. and Srivastava, A. (2004). Analysis of the Chlorophyll a fluorescence transient. In: Papageorgiou, G.C. and Govindjee (Eds.) Chlorophyll a fluorescence: a signature of photosynthesis. Springer, Dordrecht: 321-362.

• Tsimilli-Michael, M. (2020). Revisiting JIP-test: An educative review on concepts, assumptions, approximations, definitions and terminology. PHotosynthetica 58: 275-292.

Unlocking the Secrets of Quantitative Disease Resistance

Thu, 04 Dec 2025 18:22:05 GMT

Temporal Regulation of WRKY6 in Solanum pennellii

Quantitative Disease Resistance (QDR) provides durable, broad-spectrum protection against pathogens like Sclerotinia sclerotiorum , a necrotrophic fungus infecting over 400 plant species. Unlike qualitative resistance, QDR is polygenic, offering partial but sustainable resistance under field conditions. Wild tomato relatives, such as Solanum pennellii , exhibit natural variation in QDR, making them ideal models for studying the regulatory timing of defense responses. While genetic loci associated with QDR have been identified, the temporal dynamics of gene expression during early infection remain poorly understood. Einspanier et al. (2025) investigated how early activation of defense genes, particularly the transcription factor WRKY6, influences QDR in S. pennellii challenged by S. sclerotiorum.

Three genetically distinct accessions of the wild tomato relative Solanum pennellii were selected based on their contrasting lag phase durations, making them ideal models for dissecting the temporal dynamics of defense responses:

LA1282: Exhibits a prolonged lag phase (56.88 hours), representing a high-QDR genotype.
LA1941: Displays an intermediate lag phase (49.92 hours), serving as a moderate-QDR genotype.
LA1809: Characterized by a short lag phase (38.16 hours), representing a low-QDR genotype.

Test inoculum was prepared as a standardized fungal suspension of Sclerotinia sclerotiorum isolate 1980, cultivated from sclerotia on Potato Dextrose Agar (PDA) and incubated in Potato Dextrose Broth (PDB). The suspension was adjusted to an optical density (OD₆₀₀) of 1.0 and supplemented with Tween-80 to ensure uniform dispersion. Small droplets (10 μL) of the fungal suspension were applied to the center of detached Solanum pennellii leaves. Mock treatments consisted of PDB without fungal material, serving as negative controls.

Plants were cultivated under controlled environmental conditions (21°C, 65% relative humidity, 16-hour photoperiod) to ensure reproducibility and minimize environmental variability. This setup allowed for precise monitoring of disease progression and gene expression dynamics in response to S. sclerotiorum infection. The screening system consisted of a PlantExplorer Pro+ , providing high-resolution Fv/Fm (chlorophyll fluorescence) imaging to monitor photosynthetic stress every 12 hours post-inoculation (hpi), and loss of photosynthetic efficiency as an indicator of pathogen impact. A Navautron Automated Lesion Tracker was used to record lesion expansion and to track disease progression.

Resistant genotypes (e.g., LA1282) exhibited a prolonged lag phase (56.88 hours), delayed photosynthetic stress, and sustained Fv/Fm values, while susceptible genotypes (e.g., LA1809) showed rapid lesion expansion and early decline in photosynthetic efficiency. Transcriptomic analysis identified 71 differentially expressed genes (DEGs) in the resistant genotype at 48 hours post-inoculation (hpi),

including pathogenesis-related (PR) genes, oxidative stress response genes (e.g., DOX1, LOX1), and transcription factors (e.g., WRKY6). WRKY6 emerged as a central regulator, with elevated basal expression in resistant genotypes, suggesting a primed defense state. Gene Set Enrichment Analysis (GSEA) further confirmed the activation of defense-related processes.

Conclusion

The study demonstrates that quantitative disease resistance (QDR) in Solanum pennellii against Sclerotinia sclerotiorum is driven by temporal shifts in gene expression, particularly during the lag phase of infection. The researchers identified WRKY6 as a central regulator of defense-related genes, with elevated basal expression in resistant genotypes contributing to delayed lesion onset. These findings underscore the importance of early defense activation and genotype-specific regulatory dynamics in shaping QDR, ultimately enhancing the plant’s ability to delay pathogen invasion.

For further details, please read the original publication:

Einspanier, S., et al. (2025). Temporal Shifts in Gene Expression Drive Quantitative Resistance to a Necrotrophic Fungus in a Tomato Crop Wild Relative. BioRxiv. DOI: 10.1101/2025.01.01.522345

Exploring the Impact of UV-A Light on Basil (Ocimum basilicum L.)

Thu, 27 Nov 2025 10:02:36 GMT

The Role of UV-A Light in Plant Physiology

Light is a fundamental environmental factor that influences plant growth, development, and stress responses. While the effects of ultraviolet (UV) radiation on plants have been extensively studied, the specific impacts of UV-A light (315–400 nm), particularly its wavelength and intensity, remain less understood. UV-A radiation is known to influence various plant processes, including photosynthesis, photomorphogenesis, and secondary metabolite production. Unlike UV-B, which primarily induces stress responses, UV-A can act as a photoregulatory signal, modulating plant growth and development. Recent advancements in LED technology and high-throughput phenotyping have opened new avenues for investigating how UV-A radiation affects plant physiology, morphology, and biochemical composition. These effects of UV-A are highly species-specific, dose-dependent, and influenced by environmental conditions.

A study conducted by Vodnik et al. (2023), examines the effects of supplemental UV-A light of different wavelengths (365 nm and 385 nm) and intensities on basil ( Ocimum basilicum L. ). It combines conventional physiological measurements, biochemical analyses, and high-throughput phenotyping to provide a comprehensive understanding of basil’s response to UV-A radiation. Four treatments combine baseline red–blue LEDs with UV-A at 365 nm, 385 nm, or both, at total intensities ranging from 3.5 to 16 W m⁻² (E1–E4). Plant traits are assessed using 3D multispectral scanning, chlorophyll fluorescence imaging using the CropReporter , and biochemical analyses of pigments and phenolic compounds.

Basil showed high tolerance to UV-A. across all treatments, including the highest intensity. No negative effects were detected in biomass, plant height, leaf area, or digital volume. Morphological development remained consistent with the control lighting conditions. UV-A did not reduce photosynthetic efficiency. Higher UV-A doses yielded increased effective quantum yield (Fq'/Fm') and electron transport rate (ETR), indicating improved photochemical performance. The rise in xanthophyll cycle pigments (VAZ) and VAZ-to-chlorophyll ratios suggested activation of photoprotective mechanisms that help prevent photoinhibition.

Although anthocyanin accumulation was not observed, the Anthocyanin Index increased under UV-A, likely reflecting structural or spectral changes. UV-A exposure induced a dose-dependent rise in hydroxycinnamic acid derivatives, such as rosmarinic acid-glucoside and caffeic acid-glucoside, which act as UV screens and antioxidants. Glycosylation enhances their stability and strengthens basil’s oxidative stress defense. Basil’s UV-A tolerance appears to rely on enhanced thylakoid electron transport, increased photoprotective pigments, and the upregulation of antioxidant phenolics. Together, these mechanisms maintain photosynthetic efficiency and mitigate UV-induced stress.

The findings indicate that UV-A can be safely integrated into LED lighting strategies to improve plant resilience and increase valuable secondary metabolites. High-throughput phenotyping tools such as multispectral 3D scanning and chlorophyll fluorescence imaging support precise monitoring and optimization of light regimes in indoor farming systems.

Conclusion

This study demonstrates that basil exhibits remarkable tolerance to UV-A radiation, thanks to a combination of enhanced photosynthetic efficiency, protective pigment accumulation, and metabolic adaptation. The integration of high-throughput phenotyping with conventional physiological and biochemical analyses provides a holistic understanding of UV-A responses, paving the way for innovative lighting strategies in horticulture.

For further details, please explore the original publication:

Vodnik, D., et al. (2023). Phenotyping of basil (Ocimum basilicum L.) illuminated with UV-A light of different wavelengths and intensities. Scientia Horticulturae. DOI: 10.1016/j.scienta.2022.111638

High-throughput Phenotyping towards Herbicide discovery

Thu, 20 Nov 2025 12:44:17 GMT

The Role of Chlorophyll Fluorescence in Herbicide Screening

The global challenge of herbicide resistance, coupled with environmental concerns, has intensified the demand for innovative, sustainable, and effective herbicides. Traditional herbicide discovery methods are often slow, resource-intensive, and environmentally taxing. Multichannel plant imaging, for example chlorophyll fluorescence imaging, can offer a robust indicator of plant health and stress responses. Plant imaging offers rapid, quantitative, and high-throughput screening of novel herbicidal compounds. This approach not only accelerates the identification of promising candidates but also minimizes environmental impact by reducing the need for extensive field trials.

Chlorophyll fluorescence , particularly the Fv/Fm parameter, quantifies the maximum quantum efficiency of photosystem II (PSII). Herbicides that disrupt photosynthesis induce a measurable decline in Fv/Fm, making it a powerful early marker of herbicidal activity. This method is highly sensitive, non-destructive, and capable of detecting stress responses before visible symptoms appear.

A Case Study

A recent study conducted at Ghent University (Backx et al., 2024; Backx et al., 2025), using the CropReporter system, demonstrated the efficacy of plant imaging in screening novel herbicidal compounds. The researchers focused on 3-acyltetramic acids and their prodrugs, which are biologically derived and show promise as sustainable herbicides. Leaf disks were obtained from fully developed leaves of tomato plants ( Solanum lycopersicum ‘Moneymaker’ L. ), cultivated under controlled greenhouse conditions (21 °C, LED lighting, and regular fertilization). Standardized disks (0.6 cm or 1.1 cm diameter) were punched from leaves and placed in 24- or 96-well plates containing sterile distilled water for a destressing period prior to treatment. Fv/Fm measurements were taken at 24, 48, 72, 96, and 144 hours post-treatment. IC₅₀ values (the concentration required to reduce Fv/Fm by 50%) were calculated using four-parameter log-logistic models.

Key Findings

The study demonstrated that Fv/Fm imaging provides a fast, reliable, and quantitative method for herbicide screening . Compounds inducing strong reductions in Fv/Fm corresponded with high herbicidal activity, validating the use of this parameter as a proxy for phytotoxicity. The approach also enabled early detection of herbicidal effects with minimal compound use, quantitative potency ranking, and structure-activity analysis.

While in vitro assays provide valuable insights, translating these results to field conditions requires careful consideration. Some prodrugs, which showed promising activity in leaf disk assays, did not outperform the original compound in spray tests on Amaranthus retroflexus seedlings. Targeted field validations are still needed to ensure real-world efficacy. However, high-throughput phenotyping does reduce the time and cost associated with herbicide development, enabling researchers to focus on the most promising candidates. And, by minimizing the use of compounds and reducing reliance on field trials, the approach aligns with the principles of sustainable agriculture. The integration of phenotyping data with other omics technologies (e.g., genomics, metabolomics) can further refine the understanding of herbicide modes of action and resistance mechanisms.

Conclusion

B y using multispectral plant imaging parameters in herbicide d iscovery as a quantitative proxy for plant health, researchers can rapidly screen novel compounds, optimize their efficacy, and reduce environmental impact. As demonstrated by the work at Ghent University, this approach not only benefits early-phase herbicide discovery. It may also pave the way for more sustainable and effective weed management strategies.

For further details, please explore the original research publications:

Backx, S., et al. (2024). Synthesis of Mixed Phosphonate Esters and Amino Acid-Based Phosphonamidates, and Their Screening as Herbicides. International Journal of Molecular Sciences. DOI: 10.3390/ijms25094739
Backx, S., et al. (2025). Synthesis and Herbicidal Assessment of 3-Acyltetramic Acid Prodrugs. ACS Omega. DOI: 10.1021/acsomega.5c02851

The new CF2GO: efficiency of photosynthesis measured with scientific accuracy

Thu, 10 Jul 2025 12:39:51 GMT

In June 2025, we launched the CF2GO (Chlorophyll Fluorescence 2 Go) at the Greentech in Amsterdam, where the jury nominated it for the innovation award out of 47 products.

The CF2GO is a system that accurately measures how efficiently a crop uses light for photosynthesis, in real time and from up to a meter away from the crop. No leaf clips, and not touching the plants.

It brings the same scientific quality as our lab systems into the greenhouse. The CF2GO can hang in a fixed spot or move along a rail or boom, and it is already running at several commercial growers.

Johan Langelaan, cultivation manager at Maarel Orchids, explains why they use it: "We can now see in real-time when the plants require more or less light for photosynthesis, allowing us to adjust screening and lighting immediately." And: "You get hard facts and measured values from the system, and you can immediately see what they mean." ( https://www.onderglas.nl/gewas-krijgt-exact-de-hoeveelheid-licht-die-het-nodig-heeft/ )

Figure 1: Two of the CF2GO's mounted in two different commercial greenhouses.

What is chlorophyll fluorescence?

When a leaf absorbs light, that energy can go three ways. Most of it is used for photosynthesis. Some is released as heat, and a few percent comes back as red light: chlorophyll fluorescence. You can't see it by eye, but a camera with the right filter can.

The three routes of energy are in direct competition. When less energy goes to photosynthesis, more goes to heat and fluorescence. That is why the fluorescence signal tells us how well photosynthesis is running. (More in our introduction to chlorophyll fluorescence: https://www.phenovation.com/introduction-to-chlorophyll-fluorescence )

How does the CF2GO measure it?

The CF2GO gives the plant one short, very bright flash of light, about 6,500 µmol m⁻² s⁻¹ at 1 meter (roughly three times full sunlight). The flash lasts about a second, and does not harm the plant.

At the start of the flash, the fluorescence is at its lowest. As the photosystems fill up with energy, the fluorescence rises to a maximum. The CF2GO captures that rise, which is called the OJIP curve, and calculates more than 20 parameters from it. (For more information see OJIP part 1 and 2: https://www.phenovation.com/ojip and https://www.phenovation.com/chlorophyll-fluorescence-measuring-methods-ojip-part-2)

Think of it as a fitness test for the plant, like for an athlete. How fit the plant is depends on its conditions, stress, and how well the photosynthetic system is working.

What it looks like in practice

Stress, before you see it on the plant

To demonstrate what chlorophyll fluorescence can do, we sprayed soybean plants with glyphosate (a strong herbicide that kills the plant). Already on the second day, the efficiency of photosynthesis dropped significantly. NPQ (heat release) went up and ETR (electron transport rate) went down. The plants still looked fine by eye (and RGB imaging). Yellowing of the leaves began after four days.

Ofcourse, glyphosate kills a plant, so this is an extreme case. But for stress you can fix, like too much light, drought or heat, that early signal gives a window to act.

Phalaenopsis in the afternoon

Phalaenopsis is a CAM plant, meaning it takes up CO₂ at night and stores it in the form of malate, so that it can be used during the day. The plant does this, so that it can close its stomata during the day, helping to save water when it is hot.

In a climate cabinet with constant light, the efficiency of photosynthesis of healthy phalaenopsis plants dropped at the end of the day (Figure 2). This was due to the malate reserves running out. The plant did not have enough CO2 to produce sugars, which is why the efficiency dropped.

When we dimmed the lights in steps, the efficiency did not drop. This means much energy was saved by dimming the lights in the afternoon, when the plant did not use the light anyways (it could even be harmful).

In a commercial phalaenopsis greenhouse, we saw the same afternoon dip (Figure 3). And, before efficiency dropped, TFm, which is the time to maximum fluorescence, started dropping one hour earlier (meaning the plant could already handle less light). This gave the grower an early signal to screen more or dim the lights.

This grower also uses a parameter called deviation ETR (Figure 4). Deviation ETR is a measure of how far the current ETR (electron transport rate) deviates from the theoretical maximum. The system gives a warning when the deviation exceeds more than 30%.

On partly cloudy days, the value shot above the threshold (Figure 5 at the red arrows), whenever full sun hit the plants while the screens were still open.

All data in one place

The CF2GO sends its data to MyLedgnd, where you see it next to PAR, temperature, humidity, CO₂ and your other sensors. So you can steer screens and lighting on how the plant responds, not only on fixed settings like DLI or VPD. Data can also be downloaded or reached through an API, for example to link it to the climate computer.

For more information about the CF2GO, visit: https://www.phenovation.com/cf2go or https://www.cf2go.nl/

Introduction to Chlorophyll Fluorescence

Wed, 14 May 2025 10:22:30 GMT

What is chlorophyll fluorescence?

In agriculture and horticulture, optimizing photosynthesis and preventing stress is essential for maximizing crop yield. Efficient light capture and energy conversion are key to healthy plant growth and high productivity.

Nowadays, technological advancements enable growers and researchers to quantify and monitor plant processes in detail, for instance photosynthetic activity, stomatal conductance, and transpiration. This provides insights into plant functioning that was previously invisible. One particularly influential technology in plant sciences is the analysis of chlorophyll fluorescence. This is because it relates to photosynthesis, and can be measured from a distance.

But what exactly is this fluorescence signal, and why is it used so often in plant science?

When a plant is illuminated, chlorophyll molecules absorb light. While most of this light energy is used for photosynthesis, some is dissipated as heat, or re-emitted as fluorescence (Figure 1). It is not reflected light, but actually emitted by the plant itself. The plant is giving off light (in a different color) (Figure 2).

Chlorophyll fluorescence is emitted at a relatively long wavelength, at the edge of the visible light spectrum. Peaks of emission lie at 680 and 730 nm (Figure 3).

The fluorescent light originates from the plant's photosystems. These are involved in capturing light, and driving photosynthesis.

Basis of Chlorophyll Fluorescence Measurements

To understand how chlorophyll fluorescence measurements work, we need to explore the basis of photosynthesis.

Fluorescence is emitted from the chlorophyll molecules, specifically the protein complexes known as photosystems I and II, which are embedded in the thylakoid membranes (Figure 4). This complex drives the light phase of photosynthesis.

Photosynthesis processes are split in two phases:
• The light phase
• The dark phase (also called Calvin cycle)

When we observe chlorophyll fluorescence, we are essentially looking at the light dependent phase of photosynthesis.

At this phase, energy from light is converted into chemical energy, as a way to store it. These chemicals are then used in the other phase, the Calvin cycle, to bind CO2 and produce sugars. Therefore, these two phases are dependent on each other.

When the photosystems capture light through their antennae, their reaction centers become excited with energy. This energy must be effectively managed by the plant, otherwise it can react with molecular oxygen, resulting in 'reactive oxygen species' (ROS). These are harmful and cause cellular damage. Hence, the plant developed processes to safely direct the energy, and they operate at very high speeds. This transfer of energy from chlorophyll to photochemistry (or heat) is referred to as quenching.

After the excitation of the reaction center of the photosystems, this energy is used to carry electrons through the electron transport chain, across the thylakoid membrane (Figure 5). This is one of the classic courses of photosynthesis in biology.

During the movement of the electron through the chain, the energy is used to pump hydrogen into the lumen, which produces a hydrogen proton gradient inside the lumen. This then powers the enzyme 'ATP synthase', just like a water wheel, to produce ATP. Simultaneously, light energy is also used to form NADPH.

The energy stored in ATP and NADPH is then used in the Calvin cycle (dark phase) to bind CO2 and produce glucose.

This entire process occurs at a specific rate, which limits the proportion of absorbed light energy that can be used exclusively for photosynthesis. As previously mentioned, some of this light energy is dissipated through other pathways: as heat or as fluorescent light.

Energy dissipation through heat and fluorescence happens continuously (Figure 6). To prevent stress and photo-damage, the plant tries to actively regulate the energy release via a process known as non-photochemical quenching (NPQ) . NPQ represents the dissipation of heat, but in a controlled way. Additionally, some of the excited chlorophyll molecules release the absorbed energy as photons, which is what we see as fluorescent light. This light is primarily emitted by photosystem II and accounts for only a small fraction of the total absorbed energy (approximately 1-2%).

The different energy pathways are in direct competition with each other, meaning that an increase in one pathway leads to a decrease in the others. This competition is what enables us to calculate, and quantify, how a plant balances energy absorption, conversion, and dissipation. This provides valuable insights into the plant's physiological functioning.

Why does fluorescence yield change?

When a plant is transferred from dark to light, fluorescence emission increases sharply within the first few seconds (Figure 7). Understanding what happens inside the chlorophyll molecules during this transition is important.

The rise in fluorescence yield during the first few seconds after light exposure has been explained by electron acceptors becoming 'occupied' within the photosynthetic pathway (Kautsky et al., 1960). When photosystem II (PSII) absorbs light and electrons are accepted into the electron transport chain, other electrons cannot be accepted until the first ones are passed onto the other carriers in the chain. During this period, the reaction center of PSII is considered 'closed.'

As reaction centers close, photosynthesis efficiency reduces temporarily. And because light energy cannot flow into the photosynthesis pathway, the fluorescence yield increases (remember the three fates). Because the plants were in the dark before, non-photochemical quenching (NPQ) is not present; these processes take more time to activate. That is why fluorescence yield increases. Once NPQ mechanisms start to engage, fluorescence yield decreases and eventually stabilizes at a steady state.

This reaction makes it possible to quantify the maximum potential photosynthetic efficiency of plants, as well as the balance between photosynthesis in the light and heat dissipation. The interesting thing is that fluorescence yield also changes when plants are stressed. Some PSII centers may become inactive or impaired, which changes the fluorescence dynamics.

In future blogs, we will dive into how fluorescence is measured and what we can calculate with these signals.

Chlorophyll Fluorescence measuring methods: PAM fluorometry

Fri, 16 Aug 2024 08:07:41 GMT

Chlorophyll Fluorescence Measuring Methods

Measuring protocols for chlorophyll fluorescence have been developed over the last century by biophysicists at universities. In the past 20 years, this fundamental research has been implemented into field applications. Various approaches have been developed to analyze the photosynthetic process via chlorophyll fluorescence, and they can be broadly categorized into three types: single turnover flashes, fast induction of chlorophyll fluorescence, and photosynthetic steady-state quenching analysis.

While these types can be combined, they are most commonly implemented through two popular technical platforms: Pulse-Amplitude Modulation (PAM), and the fast polyphasic fluorescence rise by continuous excitation (called OJIP).

From 2009 to 2019, a total of 4490 scientific papers were published using chlorophyll fluorescence techniques (Zavafer et al. 2020). From those papers, PAM was used in 2459 papers (55% of all publications), and OJIP accounted for 1073 papers (24%).

In this blog, we will delve into the most widely adopted method: PAM fluorometry.

PAM Fluorometry

The PAM fluorometry protocol uses modulated light to measure fluorescence. The measuring light consists of very short pulses (in the microsecond range) that can be applied at different speeds. These measuring pulses itself are so short that they have no impact on photosynthesis, making sure that the fluorescence yield reflects the actual photosynthetic activity at the time of measuring.

Figure 1 gives an overview of the full PAM fluorometry protocol, divided into sections. It covers dark adapted measurements, light adapted measurements, and dark recovery measurements. We will highlight each section below.

PAM - Dark Adapted Measurement

A parameter from chlorophyll fluorescence analysis that is used a lot in science is the potential maximum efficiency of photosystem II (Fv/Fm). Photosystem II (PSII) is the key component of photosynthesis, and responsible for splitting water and transferring excited electrons into the electron transport chain (see introduction blog).

Fv/Fm therefore serves as a measure of plant photosynthetic potential, and reflects the plant’s ability to fully utilize light when all photosystems are open . To ensure all photosystems are open, the plant must be kept in complete darkness for 15-30 minutes or longer. This period allows for the release of any previous quenching, making the measurement possible.

After dark adaptation, the base fluorescence level (Fo) is measured using the very short measuring-light pulses (Figure 2). The Fo reflects the minimal fluorescence level emitted by PSII when all reaction centers are 'open'. The light pulses are so short that no energy transfer occurs in the reaction center beyond fluorescence emission.

Next, a saturation light pulse (>6000 μmol m2 s1; which is 3 times the intensity of the sun) is applied to fully saturate the photosystems. This happens because the photosynthethic processes has to adapt to this extreme light, but it takes some time. The system is flushed with electrons, effectively 'clogging' the chain, shortly blocking photosynthesis. Because energy can not flow towards photosynthesis anymore during this time, fluorescence yield increases to a maximum; Fm (remember the three fates of energy) (Figure 3). This measurement is done in dark adapted plants, because other energy transfer processes (heat dissipation: NPQ) must be zero.

The difference between Fo and Fm, known as Fv, is expressed relative to Fm (Equation 1), which calculates the potential maximum efficiency of PSII. This value represents the potential maximum efficiency of PSII because no other competing processes are active during the measurement.

It can be seen as a 'fit-test' for the plant. We often use the following example:

If we want to know the fitness of this biker, we let him perform a fit-test. What determines the difference between minimal and maximal effort of the biker?
- Fitness and health

- Functioning of his bike

- Environmental factors (is there a lot of wind during the test?)

The same is true for the plant.

In the following example, we sprayed glyphosate on the plant in different concentrations. Glyphosate is a herbicide that kills the plant. After one week, all plants were dead. Leaf yellowing started from day 4. However, photosynthesis responded before that. Before we could see anything on the plant.
Looking at the maximum efficiency of photosystem II (Fv/Fm), we can clearly see the effects of glyphosate on the photosynthetic machinery.

 PAM - Light Adaptation Measurement

To measure a plant's adaptation to light, environmental light (actinic light) is turned on at a specified intensity. The actinic light induces photosynthesis. During the actinic period, multiple 'efficiency' measurements (with minimal- and maximal pulses) are taken to assess the quenching processes happening in the plant.

The values of minimal and maximal fluorescence measured in the light are named differently than in the dark, namely: Fs' and Fm' (Figure 2). The difference between Fs' and Fm' is called Fq'.

The behavior of the line is reflecting the plant's adaptation process. Under light conditions, the plant cannot use all light energy for photosynthesis (see introduction blog), and therefore also initiates processes to safely dissipate excess energy, called non-photochemical quenching (NPQ). NPQ increases when a plant is adapting to light, which is why the line is sloped. Eventually, fluorescence yield reaches a steady state.

From the values of Fq' and Fm', the actual photosynthetic efficiency (called ΦPSII) can be calculated (Equation 2).

Using ΦPSII, the electron transport rate (ETR) can be calculated (Equation 3). ETR is a widely used parameter in plant science, with its main advantage over ΦPSII being that it incorporates light intensity and light absorption rate, making it more representative of actual photosynthetic activity in a given environment. It is highly correlated with actual carbon fixation.

ΦPSII alone can be beneficial in controlled conditions (where light intensity is fixed), or when comparing relative efficiencies across treatments, as it remains a simpler assessment that does not require light intensity and light absorption measurements.

where alpha (α) is the absorbance of the leaf, PSII ratio (β) is the proportion of light absorbed by PSII, and actinic light intensity ( I) is the photosynthetic photon flux density (PPFD) of the actinic light in μmol m-2s-1.

NPQ is calculated by comparing the maximal fluorescence in the light (Fm') with the maximal fluorescence in the dark (Fm):

 Dark Recovery

In the dark, after the period of illumination, the plant gradually relaxes (or ‘un-quenches’) from the absorbed light energy. This recovery can be monitored to provide insights into different quenching mechanisms, which are characterized by fast- and slow-relaxing components.

Dark recovery can be measured using a quenching protocol, where the dark-adapted and light-adapted stages are performed consecutively. Immediately after the actinic light is turned off, a low-intensity far-red light pulse (approximately 5 seconds) is applied. This pulse ‘flushes’ electrons from the electron transport chain. This is because far-red light is used exclusively by Photosystem I (PSI), effectively absorbing all electrons from electron carriers in the chain.

Minimum fluorescence is measured after this far-red treatment, and is called Fo’.

The difference between Fm’ and Fo’ is referred to as Fv’. The measurement of maximum fluorescence after the far red pulse is called Fm'' (two primes instead of one).

After collecting these values, further photochemical and non-photochemical parameters can be calculated.

Further Photochemical Quenching Parameters

Other often used photochemical parameters that are calculated are qP and qL.

qP represents an estimate of the fraction of PSII reaction centers that are open and ready for photochemistry, derived using the following equation (Equation 5):

However, studies found that the correlation between qP and the fraction of open PSII centers was not always linear. This non-linearity is due to qP being based on the “puddle model”, which treats every PSII reaction center as independently, with its own antenna system and where there is no energy transfer among different complexes (Kramer et al., 2004; Baker, 2008). However, evidence suggests this model does not always reflect physiological reality (Butler, 1978; Lavergne & Trissl, 1995; Lazer, 1999; Barber, 2003; Kramer et al., 2004).

Barber (2003) showed that PSII centers form connections within the thylakoid membrane, and subsequent work found that they are embedded in a shared antenna matrix capable of excitation-energy transfer between units (Baker, 2008). This configuration is known as the “lake model” (or “connected units model”) and is considered more representative for most oxygen photosynthetic organisms, particularly higher plants.

Based on the lake model, Kramer et al. (2004) derived a new parameter, qL (Equation 6), which more accurately estimates the fraction of open PSII centers:

Although qL is widely acknowledged, and offers a more theoretically refined measure, its practical application and interpretation also come with complexities and some limitations (see paper). Despite the theoretical advantage of qL, qP is still often used in studies. Researchers look for relative changes or trends compared to a control and with consistent measurements, qP can still be a good indicator for stress among treatments.

Further Non-Photochemical Quenching Paramters

During dark‐recovery (un‐quenching), different non‐photochemical quenching mechanisms recover at different rates. These variations allow us to distinguish between fast‐relaxing and slow‐relaxing components. The NPQ value we have seen before (Equation 4) is a fast quenching mechanism, and also rapidly relaxes upon darkening.

However, if a parameter is desired that includes both fast and slow quenching processes (such as state‐transitions and photoinhibition), the coefficient qN (Equation 7) can be used. This parameter effectively differentiates between these processes, as qN = qE + qT + qI.

qE (energy quenching) (Equation 8) is a fast-relaxing process that occurs within 2-5 minutes. In contrast, qI (photo-inhibitory quenching)(Equation 9) is a slow-relaxing process that can extend from several hours to days, depending on the rate of photoinhibition. This extended recovery time is dependent on the amount of damage to the PSII units that the plant must repair.

A limitation of qN is its reduced sensitivity to changes when the values are elevated, making it less responsive.

Total energy partitioning

In addition to controlled energy release, some energy is emitted in a non-regulated manner. We can derive this form of energy release relative to the other forms of energy release (PQ, NPQ), to get the quantum yield. The fraction of open PSII centers (qL) is also used. The quantum yield of this non-regulated energy release is expressed as ΦNO and can be calculated using the following formula (Equation 10):

With the quantum yield of non-regulated energy release established, the quantum yield of NPQ (ΦNPQ) can be calculated (Equation 11):

These calculations let us partition the absorbed energy into photochemistry, regulated heat dissipation, and non-regulated losses, providing valuable insight into the plant's energy distribution, since: ΦPSII + ΦNPQ + ΦNO = 1

Deriving NPQ without dark adaptation

NPQ is an important parameter when studying photosynthesis and optimizing plant efficiency. However, measuring NPQ requires determining the maximal fluorescence yield (Fm) in the dark. Dark adaptation of plants is not always practically feasible and, if done too briefly, can introduce errors. To address this, Tietz et al. (2017) proposed a method for estimating NPQ using the parameter Fo' as a reference instead of Fm. This approach leads to a new equation for NPQ:

The values for NPQ and NPQ(t) are highly linear and accurate in most cases. However, the formula is based on several assumptions, which can be a limitation. These assumptions are discussed in their paper, where an overview of the potential drawbacks is provided in their discussion section.

References

Baker, N.R. (2008). Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annual Review of Plant Biology 59: 89-113.
Barber, J. (2003). Photosystem II: the engine of life. Quarterly Reviews of Biophysics 36: 71-89.
Barber, J. (2016). Photosystem II: the water splitting enzyme of photosynthesis and the origin of oxygen in our atmosphere. Quarterly Reviews of Biophysics 49: e16
Butler, W.L. (1978). Energy Distribution in the Photochemical Apparatus of Photosynthesis. Annual review of plant biology 29: 345-378.
Kramer, D.M., Johnson, G., Kiirats, O. and Edwards, G.E. (2004). New fluorescence parametesr for the determination of QA redox state and excitation energy fluxes. Photosynthesis Research 79: 209-218.
Lavergne J. and Trissl, H-W. (1995). Theory of Fluorescence Induction in Photosystem II: Derivation of Analytical Expressions in a Model Including Exciton-Radical-Pair Equilibrium and Restricted Energy Transfer Between Photosynthetic Units. Biophysics Journal 68: 2474-2492.
Lazár, D. (1999). Chlorophyll a Fluorescence Induction. Biochimica et Biophysica Acta (BBA) - Bioenergetics 1412: 1-28.
Tietz, S., Hall, C.C., Cruz, J.A. and Kramer, D.M. (2017). NPQ(T): a chlorophyll fluorescence parameter for rapid estimation and imaging of non-photochemical quenching of excitons in photosystem-II-associated antenna complexes. Plant, Cell & Environment 40: 1243-1255.
Zavafer, A., Labeeuw, L. and Mancilla, C. (2020). Global Trends of Usage of Chlorophyll Fluorescence and Projections for the Next Decade. Plant Phenomics 2020: 1-10.

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PAM Fluorometry

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