Name: High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot (Video) | JoVE Journal
Uploaded: 2026-01-09T12:30:00.000+00:00
Duration: 7 min 12 s
Description: Learn high-throughput photosynthetic phenotyping in crops using an autonomous robot with LIFT sensors. This method enables rapid, non-invasive field screening of photosystem II efficiency across diverse genotypes.

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5 minutes

We thank Caterra AG for providing the robot chassis, associated technical support, and help with its conversion for our measurements. We thank Nicola Storni from the Crop Science research group of the Swiss Federal Institute of Technology Zurich for his support with setting up the GNSS position logging system on the robot. Special thanks are due to Christoph Barendregt from DSP Delley Seeds and Claude-Alain B&#233;trix from Agroscope for providing plant material from the Agroscope/DSP partnership for the trials within the PhenoSoy project, which was funded by the Federal Office for Agriculture (FOAG). The method was developed within the project Increasing sustainability and nitrogen use efficiency by improving peas for crop rotation (ECOPRot) funded by the ETH World Food System center through a donation from Bayer AG and an ETH Zurich Career Seed Award.&#160;Large language models were partly used to assist with coding and phrasing. All outputs were checked, verified and finalized by the authors.

1. Setting up the PPFR sensor and data logger
<ol>
	<li>Connect the PPFR sensor and the powerbank to the data logger. Connect the data logger to the laptop computer and start the data logger using the data logger desktop client.</li>
	<li>Install the camera tripod next to the experimental field with the PPFR sensor on top and the data logger and powerbank on the ground below. 
	NOTE: Make sure that the PPFR sensor is in an upright position and not shaded or otherwise affected by any nearby object. The PPFR sensor would need a gimbal if mounted to the robot directly to measure accurate PPFR values.</li>
</ol>
2. Setting up the robot and LIFT sensor assembly
<ol>
	<li>Load the waypoint coordinates (.geojson format) for the measurement onto the robot. They can be the same as those used for GNSS-guided sowing of the trial.</li>
	<li>Mount the LIFT sensor assembly on the front of the robot at a height of about 60 cm above the crop canopy (see Figure 1A).</li>
	<li>Place the laptop computer, car battery, and power inverter on top of the robot and connect the power inverter to the car battery and to the LIFT sensor assembly. 
	CAUTION: The excitation beam of the LIFT sensor is dangerous to the eyes. Looking directly into the excitation beam has to be strictly avoided.</li>
	<li>Position the GNSS antenna kit on top of the robot, connect it to the laptop computer, and start the GNSS logger desktop client.</li>
	<li>Connect the LIFT sensor to the laptop computer and start the LIFT desktop client with the following settings: 
	LIFT sensor: excitation power = 40,000 µmol photons m&gt;-2 s-1; flashlet length = 1.6 µs; number of excitation flashlets per single measurement = 300; number of relaxation flashlets = 80; time between excitation flashlets = 2.5 µs; time between relaxation flashlets: ji = 101.28 + 0.0215 × i μs, where ji  is the interval length of the ith flashlet; sensor gain = 10 or 25; measuring interval = 0 s. 
	Spectrometer: spectral integration time = 100 ms, spectral range from 400 to 800 nm. 
	RGB cameras: exposure = automatic; shutter = automatic; gain = automatic; brightness = 0; frame rate = 20 s-1; triggering mode = allowed (triggers the camera at each LIFT measurement). 
	NOTE: The quantity directly measured by the LIFT sensor is the ChlF yield (increase in total ChlF due to the excitation beam for each flashlet20. Then, calculate Fq'/Fm' as 
	Fq'/Fm' = (Fm' - F') / Fm' 26 
	where F' is defined as the ChlF yield of the 1st flashlet and Fm' as the average of the ChlF yields of the 301st and the 302nd flashlets28. The total duration of one ChlF measurement (including the relaxation phase) is around 21 ms, but the duration of the crucial 300 flashlet excitation phase is only 750 µs.</li>
</ol>
3. Conducting LIFT measurements
<ol>
	<li>Manually drive the robot to the beginning of the first row of plots in the experimental field using the remote controller.</li>
	<li>Activate the measuring script for the LIFT sensor and the spectrometer in continuous mode.</li>
	<li>Start the autonomous robot navigation at 0.5 m s-1 velocity using the robot control website.</li>
	<li>During measurement, periodically check the plot representing the ChlF yield over time in the LIFT sensor assembly desktop client to confirm that it has the expected shape (see Figure 1B). Adjust the sensor gain if signals are too weak.</li>
	<li>Periodically hold the white reference panel under the excitation beam at the height of the crop canopy while the robot is turning at the field border.</li>
</ol>
4. Data integration and preprocessing
NOTE: The R code is available on GitHub (https://github.com/beat2keller/lift_data_processing)
<ol>
	<li>Read in the LIFT transient and spectral data from all *_data.csv and *_spectral.csv using R data.table package34.</li>
	<li>Read in the GNSS and weather data together with the .geojson plot maps and the experimental design.</li>
	<li>Combine the datasets and perform statistical analysis, e.g., as described in12, or below.
	<ol>
		<li>Extract Fq'/Fm' from each recorded transient.</li>
		<li>Join each GNSS point to its corresponding plot based on .geojson polygons using spatial containment. Do not forget to account for the distance between the LIFT sensor assembly and the GNSS antenna on the robot.</li>
		<li>Determine the heading of the robot from consecutive positions. Merge the experimental design with the GNSS data by the assigned plot identifiers. 
		​NOTE: This step links each spatial position to the respective treatment (e.g., genotype) and replication (e.g., block) information.</li>
		<li>Combine the high-resolution data from the PPFR sensor with low-resolution PPFR data from the nearest weather station to produce a fused incident PPFR time series (optional). 
		NOTE: Low-resolution meteorological data for Switzerland can be derived from the agrometeo.ch (https://agrometeo.ch/de) network as described in​35.</li>
		<li>Filter the white reference measurements from the spectral reflectance dataset and merge them with the PPFR data using time stamps to create a lookup table for the spectral reflectance under different incident light intensities as described in28.</li>
		<li>Correct the raw spectral reflectance data based on the lookup table.</li>
		<li>Calculate the MERIS terrestrial chlorophyll index (MTCI) and the normalized difference vegetation index (NDVI) from the corrected spectral reflectance data using the formulas 
		<img alt="Vegetation indices MTCI, NDVI formulas; remote sensing, spectral analysis; diagram." src="/files/ftp_upload/69530/69530eq1v2.jpg" style="margin:0;" data-altupdatedat="1784001604000" data-alt="Equation 1"> 
		​where Rλ denotes the mean reflection at wavelength λ nm.</li>
		<li>Merge the ChlF, GNSS position/experimental design and PPFR/spectral reflectance data by performing a nearest neighbor join with a defined tolerance window (e.g., 1 ms) based on time stamps.</li>
		<li>Identify and remove rows with outliers in Fq'/Fm' from the dataset.</li>
		<li>Extract genotype-specific photosynthetic response trends (G: PPFR) from the outlier-corrected ChlF data by fitting the model 
		Fq'/Fm' = β0 + β1 Date +  β2 (Heading x Hour) + β3 (Genotype × PPFR) +  β4 MTCI + β5 PPFR + ε 
		to the outlier-filtered dataset.</li>
		<li>Estimate the slope (β3) of the (Genotype × PPFR) predictor term using estimated marginal trends (emtrends in R) to obtain a quantitative measure of the genotype-specific response to irradiance. Including the MTCI in the model accounts for differences in chlorophyll content and canopy status that influence Fq'/Fm' as described previously12. 
		NOTE: Heading denotes the driving direction of the robot (derived from GNSS bearings in step 4.3.3) to account for potential directional effects during measurements.</li>
	</ol>
	</li>
</ol>

The robot used in this study was developed by Caterra AG, a company that originated from the ETH Zurich Crop Science research group, to which most of the authors of the present paper are affiliated. The authors, however, are not directly involved in the development of the robot and declare no additional competing interests.

Throughput of the method
The LIFT method allows for a much greater measuring distance between the sensor and the target leaves than previous gas exchange and PAM-type ChlF measurement devices18,19, which in turn enables automated high-throughput measurements being conducted both in the field and in indoor environments12,32,37. This eliminates&#160;the need for physical interaction with the canopy. The throughput of the method increases with robot driving speed. To ensure accurate measurement of ChlF yield over time, the distance travelled during one Fq&#39;/Fm&#39; measurement must remain negligible compared to the illuminated area diameter (20 mm for the LIFT sensor). When the robot moves during excitation, the illuminated spot also shifts, so that ChlF yield per flashlet decreases, ultimately causing erroneous estimates of Fm&#39; and Fq&#39;/Fm&#39; at excessive speeds. At a robot speed of 0.5 m s-1, only about 2.4% of the area illuminated by the last excitation flashlet was not already illuminated by the first excitation flashlet, meaning that the systematic measurement error on Fm&#39; due to the motion of the robot will be numerically negligible (full calculation on GitHub, https://github.com/beat2keller/lift_data_processing). At a velocity of 0.5 m s-1, we measured 300 plots (1.5 &#215;&#160;2 m) per hour on a 40 &#215; 36 m field, corresponding to several thousand plots per day. Moreover, the relatively low total weight (200 kg) and compact dimensions of our measuring setup allow transport by light vehicles, facilitating multi-site trials to assess photosynthetic efficiency across environments38.
Leaf versus canopy photosynthesis
The LIFT measurements in this method are spatially restricted to the uppermost, sunlit layer of the canopy, which is responsible for about 50% to 70% of total photosynthesis39. Furthermore, the amount of light each leaf can absorb critically depends on its angle relative to the sun, with more vertical leaves generally increasing leaf area index, enhancing light penetration and canopy photosynthesis40,41. With the LIFT sensor assembly used in this method, effects of 3D canopy architecture on single-leaf or whole-canopy photosynthesis can be modelled using the reflectance measurements from the spectrometer28&#160;and/or an explicit reconstruction of 3D canopy geometry42&#160;based on the images of the stereo RGB camera system. New algorithms, such as MASt3R36, learn dense, geometry-aware feature correspondences using deep transformers, enabling more robust and accurate 3D reconstruction across wide or repetitive regions. However, even without such additional corrections, the usefulness&#160;of automated, high-throughput methods, such as the LIFT, to identify more productive and resilient crop varieties has been demonstrated11,43.
Sink limitation
Sink limitation, i.e., active downregulation of photosynthesis due to an inability of the plant to use the amount of photosynthates it could produce44,45, can mask differences in &#949;c&#160;among genotypes. Sink-limited downregulation of photosynthesis likely accentuates with increasing CO2&#160;concentration in the atmosphere45. Our&#160;method can detect sink limitation only when it leads to a decrease also in Fq&#39;/Fm&#39;; there are indications that this can&#160;in fact&#160;occur46. In any case, the relevance of sink limitation appears to strongly depend on the crop and developmental stage, with wheat during grain filling being much more affected than grain legumes45&#160;because the symbiotic N fixation of the latter constitutes a strong additional sink1,44,46. If sink limitation is expected to be of high relevance in a specific crop during the time period in which LIFT measurements are performed, further studies are needed to compare the resulting Fq&#39;/Fm&#39; values to simultaneously collected gas exchange data and check the validity of the assumption that Fq&#39;/Fm&#39; constitutes a good proxy for &#949;c.
Measurement distance and variability in canopy height
The LIFT sensor assembly used in this method is designed to conduct measurements from a distance of approximately 60 cm to the crop canopy32,47. The LIFT sensor used in the method is fairly robust against minor differences in measuring distance, but a larger measuring distance generally appears to result in somewhat smaller values of Fq&#39;/Fm&#39;47. To prevent such bias, we recommend using the response of Fq&#39;/Fm&#39; to PPFR (which is more robust to changes in measurement distance) instead of the absolute values of Fq&#39;/Fm&#39; as in12&#160;and / or to explicitly account for differences in canopy height during data analysis.
Unintended, artificial shading of leaves
Increased light intensity generally decreases Fq&#39;/Fm&#39;3,8,12. Thus, unnecessary shading by operators or the measuring setup should be avoided3,8,12. To minimize shading from the robot, the driving direction can either be set to prevent it entirely32&#160;(at the cost of reduced throughput due to unproductive return trips), or measurements can be taken in alternating directions and then statistically corrected for shading effects. In this study, the term&#160;Heading&#160;&#215;&#160;Hour within the statistical model of Fq&#39;/Fm&#39; (see step 4.3.10) ensures that shading of the target leaves by the robot is accounted for.
Conclusion
In summary, a LIFT sensor mounted on an autonomous robot enables fast and automated measurements of photosynthetic efficiency under field conditions, overcoming throughput limitations of previous approaches. Whereas factors such as canopy structure and sink limitation require careful consideration, our results demonstrate that reliable and scalable photosynthesis screenings in agronomic field trials and plant breeding nurseries are feasible. The ensemble of a high-resolution stereo RGB camera system and the LIFT as a point sensor will allow for a further increase in precision: AI-driven 3D localization of leaves will enable sampling leaves with similar orientation towards the sun and taking genotype-specific leaf inclination into account. From a research perspective, it may be particularly interesting to compare the photosynthesis of genotypes with varying canopy architecture and conduct high-throughput LIFT measurements under free-air CO2&#160;enrichment (FACE) to better understand how Fq&#39;/Fm&#39; is affected by processes downstream of PS II under field conditions and potential sink limitations. In practical application, screening for photosynthetic performance and stress tolerance in breeding nurseries up to the farmer&#39;s field for precision farming applications has great potential.

Photosynthesis provides plants not only with the energy basis for the production of biomass and the maintenance of their own metabolism, but also for nitrogen (N) fixation in legumes1&#160;and other symbiotic processes2. The yield potential of any crop depends on the proportion of photosynthetic photon flux rate (PPFR) intercepted by the canopy (&#949;i), the proportion of this radiation that is actually used in photosynthesis and transformed into biomass (&#949;c), and the proportion of the biomass energy that is partitioned into the harvested product (&#949;p)3. The &#949;p can be set equal to the harvest index (HI) if the latter is defined as the biomass of the harvested product divided by the total shoot and root biomass3. Past breeding efforts have mainly focused on increasing &#949;i and &#949;c and were highly successful in doing so3: For example, modern cereal and grain legume genotypes can reach an &#949;i in the order of 90% and a HI in the order of 60%3,4, leaving little prospect to further increase &#949;i and &#949;p3,5. By contrast, values of &#949;c observed in C3&#160;and C4&#160;crops still rarely exceed 1/3&#160;of the theoretical maximum of 9.4% and 12.3%, respectively. This indicates a large potential to further increase crop productivity if &#949;c can be increased through selection based on natural genetic variation and/or targeted optimization of elements of the photosynthesis apparatus, such as the enzyme ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO), through genetic engineering3,5,6,7. However, it has been shown that i) photosynthetic efficiency is highly dynamic under field conditions as plants constantly adapt to changing light and other environmental covariates8,9&#160;ii) photosynthetic traits measured under steady-state conditions (e.g., in indoor growth chambers with artificial lighting10) show different patterns of heritability than the non-steady-state photosynthesis observed in the field11. This limits&#160;the usefulness of indoor photosynthesis phenotyping for the selection of higher-yielding genotypes of staple food crops that in practice are generally grown outdoors in the field. As a result, it is difficult to conduct a targeted selection for photosynthetic traits unless high spatial and temporal resolution data from a large number of crop genotypes are available6,12.
Past attempts to conduct non-destructive measurements of photosynthesis in the field have largely relied on handheld gas exchange13,14,15&#160;or pulse-amplitude modulation (PAM)-type chlorophyll fluorescence (ChlF)16,17measurement devices. These approaches have the shared disadvantage that leaves on which measurements should be performed need to be manually clipped or positioned into a measurement chamber18,19. Moreover, gas exchange measurements in the field take several minutes to ensure an equilibrium in the measuring chamber. This makes the measurements laborious and slow, resulting in great difficulties to achieve a sufficient throughput to study photosynthetic regulation under fluctuating conditions in general, and particularly for a meaningful screening of hundreds or even thousands of genotypes in breeding programs19. Like the aforementioned PAM ChlF measurement devices17, the light-induced fluorescence transient (LIFT) method20&#160;makes use of the facts that i) light energy that reaches the chlorophyll in photosystem II (PS II) can either be used for photosynthesis, dissipated as heat in a process called non-photochemical quenching (NPQ) or by ChlF and ii) blocking the photosynthesis pathway by saturation from a strong light pulse resulting in reduction of electron acceptors downstream of PS II will result in a corresponding increase in ChlF. Based on this induced variable ChlF, we can calculate the photosynthetic quantum efficiency of PS II (Fq&#39;/Fm&#39;) under ambient light conditions or the maximum quantum efficiency of PS II (Fv/Fm) if leaves were kept in the dark before the measurements.
The parameter Fv/Fm was established in the early 1980s by Kitajima and Butler21, Butler22, and Bj&#246;rkman and Demmig23, who reported an optimal value of &#8776; 0.83 in non-stressed leaves across diverse species. The development of Fq&#39;/Fm&#39; (&#934;PS II) as a diagnostic of photosynthetic performance began with Genty et al.24, who showed its near-linear relationship with CO2&#160;assimilation under non-stressed conditions. Maxwell and Johnson25&#160;and Baker26&#160;later provided a practical framework for applying Fq&#39;/Fm&#39;, Fv/Fm, and NPQ, emphasizing their sensitivity to environmental stress and their value in identifying damage symptoms such as photoinhibition. Murchie and Lawson27&#160;highlighted the potential (and limitations) of Fq&#39;/Fm&#39; for field phenotyping and crop improvement. More recently, Long et al.8&#160;stressed that photosynthesis in crops occurs under fluctuating light, where dynamic changes in PS II efficiency and NPQ regulation strongly affect carbon gain, underscoring the need to interpret fluorescence parameters in relation to temporal environmental variability.
As already stated in28, Fq&#39;/Fm&#39; measured with a stationary LIFT sensor correlated well with PAM (R2&#160;= 0.89) and CO2&#160;assimilation rate measurements (R2&#160;= 0.89)29,30,31. In line with this, modelling Fq&#39;/Fm&#39; responses during field seasons has been shown to allow good predictions of &#949;c, crop productivity12,32&#160;and stress tolerance33. However, the PAM-type ChlF measurement devices use a single flash of light (saturating pulse) to inhibit the photosynthesis pathway and weaker flashes to measure minimum and maximum ChlF (measuring pulse), which takes about 1 s in total and is thus not suitable for very high throughput. In contrast, the LIFT sensor produces a fast sequence of 300 high-intensity flashlets (with 40,000 &#181;mol photons m-2&#160;s-1) to achieve a gradual saturation of the photosynthesis pathway within 750 &#181;s (= 0.00075 s) while the ChlF yield is discretely measured20. This allows for a larger distance between the measuring device and the target leaf19&#160;and enables rapid, high-throughput measurements using an autonomous carrier vehicle on which the sensor is rigidly mounted32. Previous studies (e.g., 32) were often limited in throughput under field conditions or restricted to glasshouse experiments. Although these approaches were successful in identifying candidate genes for photosynthetic regulation12, they likely missed important regulatory processes occurring under field conditions. Therefore, we hypothesize that a LIFT sensor mounted on an autonomous robot constitutes a suitable tool to measure Fq&#39;/Fm&#39; of large numbers of genotypes (several measurements per second on thousands of plants or plots per day) in the field.

<table><tbody><tr><td>camera tripod</td><td>-</td><td>-</td><td>Any type on which the PPFR sensor can be horizontally mounted will do.</td></tr><tr><td>car battery (12 V / 80 Ah)</td><td>Mercedes-Benz Group AG, Stuttgart, Germany</td><td>A 001 982 81 08</td><td>-</td></tr><tr><td>data logger</td><td>HOBO Data Loggers, Bourne, U.S.A.</td><td>HOBO U30 Station, https://www.hobodataloggers.com.au/product/hobo-u30-usb-stand-alone-data-logger/</td><td>-</td></tr><tr><td>data logger desktop client</td><td>Onset Computer Corporation, Massachusetts, U.S.A.</td><td>HOBOware Version 3.7.28, https://www.onsetcomp.com/support/help-center/software/hoboware</td><td>-</td></tr><tr><td>GNSS antenna kit</td><td>ArduSimple, Andorra la Vella, Andorra</td><td>AS-STARTKIT-LR-L1L2-EUNH-00</td><td>-</td></tr><tr><td>GNSS logger desktop client</td><td>u-blox AG, Thalwil, Switzerland</td><td>u-center GNSS, [sic] Evaluation Software Version 25.03, https://content.u-blox.com/sites/default/files/documents/u-center-25.03_ReleaseNote_UBXDOC-304424225-19688.pdf</td><td>-</td></tr><tr><td>laptop computer&amp;nbsp;</td><td>-</td><td>-</td><td>Any laptop computer running a Windows (Microsoft Corporation, Redmond, U.S.A.) version compatible with the GNSS and data logger desktop clients will do.</td></tr><tr><td>LIFT sensor assembly desktop client</td><td>Soliense Inc., New York, U.S.A.</td><td>Version.2016.04</td><td>-</td></tr><tr><td>LIFT sensor assembly&lt;br/&gt; (complete with 240 V mains adapter)</td><td>Soliense Inc., New York, U.S.A.</td><td>LIFT-REM</td><td>In addition to the actual LIFT sensor, the LIFT sensor assembly incorporates a RGB stereo camera system comprised of two Blackfly S BFS-U3-50S5C cameras supplied by FLIR Integrated Imaging Solutions Inc. (British Colombia, Canada) [now Teledyne FLIR LLC, Wilsonville, U.S.A.] and a 400 to 800 nm spectrometer with a resolution of 0.46 nm supplied by Ocean Insight, [Now Ocean Optics] (Orlando, U.S.A.).&lt;br/&gt; &amp;nbsp;The robot measures &amp;asymp; 170 &amp;times; 210 &amp;times; 90 cm (length &amp;times; width &amp;times; height) and weighs &amp;asymp; 170 kg. The LIFT sensor assembly measures &amp;asymp; 23 &amp;times; 34 &amp;times; 59 cm (length &amp;times; width &amp;times; height, as mounted on the robot).</td></tr><tr><td>power inverter&lt;br/&gt; (12 V DC to 240 V 50 Hz AC)</td><td>Green Cell CSG S.A., Cracow, Poland</td><td>INV08</td><td>-</td></tr><tr><td>powerbank</td><td>-</td><td>-</td><td>Any USB powerbank will do.</td></tr><tr><td>PPFR sensor</td><td>HOBO / LI-COR, Lincoln, U.S.A.</td><td>S-LIA-M003</td><td>-</td></tr><tr><td>robot control website</td><td>Caterra AG, Opfikon, Switzerland</td><td>-</td><td>Link is provided by the manufacturer of the robot individually for each customer.</td></tr><tr><td>robot prototype chassis&lt;br/&gt; (complete with remote controller and&lt;br/&gt; 240 V mains battery charger)</td><td>Caterra AG, Opfikon, Switzerland</td><td>Firefly (prototype, not marketed), https://caterra.org/en/technologie/</td><td>Any other field robot with appropriate payload capacity, track width and ground clearance may do.</td></tr><tr><td>white reference panel</td><td>-</td><td>-</td><td>Any white or grey reference will do.</td></tr></tbody></table>

high-throughput, in-field screening of photosynthetic efficiency in crop plants using an autonomous robot

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq&#39;/Fm&#39;), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s-1, autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.

In total, 91,205 ChlF transient measurements were acquired of 36 soybean (Glycine max (L.) Merr.) breeding lines in 7 measuring days from June 12 to 27, 2025. 74,927 data points could be georeferenced to plots, and 58,916 were filtered for data quality and successfully matched with spectral and weather data. An overview of the measurement setup and representative data is shown in Figure 1, including an image of the instrument in operation (Figure 1A), ChlF induction kinetics across 12 soybean genotypes (Figure 1B), and corresponding spectral reflectance curves (Figure 1C).
The georeferenced data revealed pronounced spatial variation in both Fq'/Fm' and NDVI across the two experimental fields (Figure 2). These spatial patterns were partly associated with the driving direction of the robot (Figure 3), which was therefore accounted for in subsequent modeling to reduce the potential effects of shading on the target leaves.
Linear mixed-effects modeling of Fq'/Fm' responses to incident PPFR (Figure 4A) explained a substantial proportion of the variance (R<img alt="Static equilibrium equation, ΣFy=0, mathematical formula, balance of forces in vertical direction." src="/files/ftp_upload/69530/69530eq10v6.jpg" style="font-size: 14px; margin: 0px; margin-bottom: -5px;" data-altupdatedat="1784001604000" data-alt="Equation 10"> = 0.45, R<img alt="Quantum mechanics; wave-particle duality; equation; symbol; educational diagram; 2/c formula." src="/files/ftp_upload/69530/69530eq11v6.jpg" style="font-size: 14px; margin: 0px; margin-bottom: -5px;" data-altupdatedat="1784001604000" data-alt="Equation 11"> = 0.60). The extracted genotype-specific slopes (Response G: PPFR) showed clear differences among breeding lines (Figure 4B), with several lines exhibiting steeper or flatter response curves compared to the panel average (Figure 4C).
Finally, canopy-level 3D reconstructions derived from RGB imaging and the Matching And Stereo 3D Reconstruction (MASt3R) algorithm36 (Figure 5) demonstrated the potential of integrating LIFT-based physiological measurements with structural phenotyping to capture canopy architecture in three dimensions.
<img alt="Agriculture fluorescence spectroscopy, time-resolved emission, reflectance spectra analysis." class="xfigimg" src="/files/ftp_upload/69530/69530fig1.jpg" data-altupdatedat="1784001604000" data-alt="Figure 1"> 
Figure 1: Overview of light-induced fluorescence transient (LIFT) and spectral reflectance measurements for soybean genotypes. (A) Example image of the LIFT device in operation. The inlet shows the blue excitation beam to induce chlorophyll fluorescence (ChlF). (B) Transient ChlF induction curves measured on 27 June 2025 for 12 soybean genotypes (mean ± SD, n between 88 and 553 per genotype, ntotal = 2,035). (C) Corresponding leaf reflectance spectra measured on 25 June 2025 for the same genotypes (mean ± SE, ntotal = 2,035). Colors indicate individual genotypes; error bars represent variation among replicate measurements. <a href="https://www.jove.com/files/ftp_upload/69530/69530fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Field variability analysis; NDVI and Fq'/Fm' choropleth maps; agricultural study; geographic data." class="xfigimg" src="/files/ftp_upload/69530/69530fig2.jpg" data-altupdatedat="1784001604000" data-alt="Figure 2"> 
Figure 2: Spatial distribution of quantum efficiency of photosystem II and normalized difference vegetation index (NDVI) values. Spatial distribution of (top) quantum efficiency of photosystem II (Fq'/Fm') and (bottom) normalized difference vegetation index (NDVI) values was measured across two experimental soybean fields comprising 120 plots and 36 breeding lines (n = 58,916). Points represent measurement locations, with colors indicating the observed value and shapes indicating the heading direction of the robot: northwest (NW), northeast (NE), southwest (SW), and southeast (SE)-ward. Black lines delineate field plot boundaries. Black dots represent the global navigation satellite system (GNSS) coordinates of the field robot. <a href="https://www.jove.com/files/ftp_upload/69530/69530fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Box plot analysis of F&lt;sub&gt;q&lt;/sub&gt;/F&lt;sub&gt;m&lt;/sub&gt;' and irradiance by heading and time of day." class="xfigimg" src="/files/ftp_upload/69530/69530fig3.jpg" data-altupdatedat="1784001604000" data-alt="Figure 3"> 
Figure 3: Quantum efficiency of photosystem II. Boxplots show the quantum efficiency of photosystem II (Fq'/Fm') and the logarithm of incident irradiance at 680 nm across measurement hours (9 - 15 h) for both north-east (NE)-ward and south-west (SW)-ward rover heading direction (nNE-ward = 14,339, nSE-ward = 25,669, ntotal = 40,008). The robot was shading the measurement spot while heading SW-ward in the morning and NE-ward in the afternoon. Each box represents the interquartile range (IQR) with the horizontal line and whiskers indicating the median and 1.5 × IQR limits, respectively. <a href="https://www.jove.com/files/ftp_upload/69530/69530fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Photosynthetic response analysis; diagrams of geographic data, breeding line comparison, PPFR correlation." class="xfigimg" src="/files/ftp_upload/69530/69530fig4.jpg" data-altupdatedat="1784001604000" data-alt="Figure 4"> 
Figure 4: Photosynthetic response slopes. (A) Spatial variation in estimated photosynthetic response slopes (interaction between genotype and photosynthetic photon flux rate (G: PPFR)) across two soybean experimental fields, derived from linear mixed-effects modeling. (B) Adjusted means and model-estimated photosynthetic response slopes for individual breeding lines, with highlighted genotypes of interest shown in color. (C) Relationship between incident PPFR and the quantum efficiency of photosystem II (Fq'/Fm') for the highlighted genotypes, with fitted square-root regression curves. <a href="https://www.jove.com/files/ftp_upload/69530/69530fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Multispectral leaf analysis; diagram; plant phenotype study using imaging techniques; data visualization." class="xfigimg" src="/files/ftp_upload/69530/69530fig5v2.jpg" data-altupdatedat="1784001604000" data-alt="Figure 5"> 
Figure 5: Canopy architecture measurement by LIFT. The canopy architecture of the soybean variety Gallec is shown. (A) The red, green, and blue (RGB) cameras of the light-induced fluorescence transient (LIFT) device acquired images while screening for photosynthesis. (B, C) The matching and stereo 3D reconstruction (MASt3R) algorithm36 was used to reconstruct 3D canopy architecture. The pyramids indicate estimated camera positions. <a href="https://www.jove.com/files/ftp_upload/69530/69530fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

This manuscript describes a non-destructive, high-throughput approach for autonomous field measurements of photosystem II quantum efficiency, spectral reflectance, and plant architecture, enabling large-scale canopy photosynthesis phenotyping in agronomic and breeding field trials.

Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential ...

We investigate photosynthesis under fluctuating field conditions to identify efficient and resilient varieties, and to discover the underlying beneficial alleles. Photosynthesis is mostly assessed through time-consuming manual PAM and gas exchange measurements, which limit the detection of seasonal dynamics and the screening of high genetic diversity. To begin, install the camera tripod next to the experimental field.Place the PPFR sensor on top of the tripod and set the data logger and power bank on the ground below the tripod. Now mount the LIFT sensor assembly on the front of the robot, positioning it at a height of approximately 60 centimeters above the crop canopy. Place the laptop computer, car battery, and power inverter on top of the robot.Connect the power inverter to the car battery. Then connect the inverter to the LIFT sensor assembly. Next, position the GNSS antenna kit on top of the robot.Connect the antenna kit to the laptop computer and start the GnssLogger desktop client and adjust the desired settings. The quantity directly measured by the LIFT sensor is the chlorophyll fluorescence yield, which is the increase in total chlorophyll fluorescence due to the excitation beam for each flashlet. Let to calculate the quantum efficiency of the photosystem II, use the given formula.While the 300 flashlet excitation phase lasts only 750 microseconds, the total duration of a single chlorophyll fluorescence measurement, including the relaxation phase, is approximately 21 milliseconds. Next, manually drive the robot to the beginning of the first row of plots in the experimental field using the remote controller. Activate the measuring script for both the LIFT sensor and the spectrometer in continuous mode.Start the autonomous robot navigation at a velocity of 0.5 meters per second using the robot control website. During measurement, periodically check the plot representing the chlorophyll fluorescence yield over time in the LIFT sensor desktop client to confirm the expected shape. If signals appear too weak, adjust the sensor gain accordingly.Periodically hold the white reference panel under the excitation beam at the height of the crop canopy while the robot is turning at the field border. Use the data table package in R to read in the LIFT transient and spectral data from all files ending in data CSV and spectral CSV. Read in the GNSS data, weather data, the GeoJSON plot maps and the experimental design file.Extract the quantum efficiency of the photosystem II from each recorded LIFT transient. Then determine the robot's heading based on consecutive GNSS positions. Merge the GNSS data with the experimental design using the assigned plot identifiers to associate spatial locations with treatment and replication information.Filter the white reference measurements from the spectral reflectance dataset and merge these measurements with the PPFR data using timestamps to create a lookup table that links spectral reflectance to different incident light intensities. Now apply corrections to the raw spectral reflectance data using the generated lookup table. Extract genotype-specific photosynthetic response trends from the outlier filtered chlorophyll fluorescence dataset with the model.Estimate the slope of the genotype by PPFR interaction term using the emtrends function in R to quantify genotype-specific irradiance response. The model includes MTCI to account for variations in chlorophyll content and canopy structure. During the summer, 36 soybean breeding lines were measured.The geo-referenced dataset revealed strong spatial patterns in both quantum efficiency of photosystem II and normalized difference vegetation index across the experimental fields due to different genotypes grown in the plots and likely additional soil and field heterogeneity. Row-wise spatial patterns were partially explained by the heading direction of the field robot causing shading of the target leaves at a specific time of the day. Linear mixed effects modeling of the season-long responses of quantum efficiency of photosystem II to increasing incident photosynthetic photon flux rate, or PPFR revealed pronounced field-level heterogeneity, explaining a substantial portion of the variance in the dataset.The extracted genotype-specific slopes showed clear differences among breeding lines, with several lines exhibiting steeper or flatter response curves of quantum efficiency of photosystem II to increasing light intensities compared to the panel average. Three-dimensional canopy reconstructions, using the MOSS T3R algorithm, showed the potential of integrating physiological and structural phenotyping. Automated LIFT measurements throughout the season reveal physiological heterogeneity within plot canopies, and importantly, distinct photosynthetic efficiencies between genotypes.Our approach captures real-time photosynthesis measurements non-invasively and autonomously within milliseconds, enabling rapid comparison of photosynthetic responses across full seasons and diverse genotypes. Future work will integrate LIFT with genomic, thermal and 3D canopy data to more precisely predict photosynthesis, stress resilience and crop productivity.

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Research

JoVE Journal

Biology

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Computer Vision-Based Biomass Estimation for Invasive Plants

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High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

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Learn high-throughput photosynthetic phenotyping in crops using an autonomous robot with LIFT sensors. This method enables rapid, non-invasive field screening of photosystem II efficiency across diverse genotypes.

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

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