The late-season experiment was conducted at the Gulf Coast Research and Education Center in Wimauma, Florida. Researchers selected four plots located on two raised beds, with each plot containing a different genotype for every plant. The four plots held 16, 18, 14 and 16 valid plants, respectively, for a total of 64. Ground and aerial imaging took place on March 5 and March 12, 2024, with each session covering two plots. Two days later, the plants were harvested for ground-truth measurements. Runners, fruits, flowers, crowns and roots were removed, leaving leaves and stems. The retained material was weighed fresh, dried in an oven for five to seven days and weighed again after reaching constant weight. Dry biomass was the outcome used in the analysis because fresh mass can vary with temperature, weather and irrigation timing.

Two imaging platforms supplied the observations. A custom ground vehicle carried three Canon cameras: one top-facing Canon R8 and two side-facing Canon 7D cameras. An RTK global navigation satellite system recorded the vehicle’s trajectory, while a dark tarp and two artificial light sources were added to reduce glare, inadequate illumination and wind-related movement. The cameras were synchronized and triggered every second as the vehicle traveled at 0.13 metres per second, producing substantial image overlap. This close-range platform captured finer leaf detail than the aerial system, but its estimated camera positions followed three linear trajectories around the plants. That limited angular coverage was sufficient for SfM photogrammetry but not for NeRF or 3DGS with SuGaR: attempts to reconstruct recognizable plant structure from the ground images with those two methods were unsuccessful.

The UAV therefore provided the imagery for all three reconstruction approaches tested there. Researchers flew a DJI Mavic 3 Pro in point-of-interest mode, directing it around a central target while the gimbal kept the plants centered. Each approximately 36-second recording was captured from 12 metres above ground at 60 frames per second and 3840 by 2160 pixels. The gimbal was tilted 25 degrees below horizontal and used threefold optical zoom. Extracting frames at five per second produced about 180 images per plot. Unlike a conventional linear mapping flight dominated by top views, the circling trajectory provided overlapping perspectives around the canopy. Surveyed plot- and field-level ground control points were used as georeferencing references for the resulting models.

SfM, NeRF and 3DGS with SuGaR shared the camera-alignment stage in Agisoft Metashape. SfM used the aligned images to generate dense point clouds directly. The other workflows used the same images and camera positions in COLMAP format. The Nerfacto implementation in Nerfstudio was trained for 30,000 iterations, with 90% of frames assigned to training and 10% held out to assess rendering quality through peak signal-to-noise ratio, structural similarity and learned perceptual image patch similarity. The input frames were automatically downscaled to 1600 by 900 pixels for NeRF training. The 3DGS and SuGaR workflow used density regularization, a high-resolution mesh and a high-poly configuration refined over 7,000 iterations. Its output was an explicit surface mesh, whereas standard 3D Gaussian Splatting represents a scene as discrete Gaussian primitives without a defined surface between them.

Individual plants were separated manually from the plot-level models. An RGB hue filter removed mature fruits, flowers and black mulch from the point clouds, while the Gaussian-splatting meshes were manually denoised to remove the same non-canopy material. Researchers then fitted a three-dimensional convex hull around each retained leaf-and-stem structure and used its enclosed volume as the predictor of dry biomass. The convex hull follows an outer envelope rather than the true internal geometry of a strawberry plant, so it can include gaps among drooping leaves, curled leaf margins and stems. Incomplete camera coverage, especially in shadows, can reduce the reconstructed extent in the opposite direction. Because the study did not independently measure true canopy volume, it could not quantify the net size or direction of these competing errors.

Canopy volumes were strongly correlated among the four reconstruction methods, with pairwise R² values from 0.93 to 0.98, but correlation did not imply perfect agreement. Mean bias differed significantly from zero for every method pair, and relative root mean square error ranged from 11.6% to 25.1% of mean volume. UAV SfM produced volumes that were 15.2% lower on average than ground SfM, and the difference increased with volume. UAV NeRF volumes were 20.8% higher on average than UAV SfM volumes. The closest agreement occurred between UAV SfM and UAV 3DGS with SuGaR: their mean bias was 3.9%, relative RMSE was 11.6%, and Lin’s concordance correlation coefficient was 0.98. The methods shared ground control points and the same convex-hull volume procedure, so their consistency cannot independently validate the georeferencing correction.

Linear regression converted each canopy-volume estimate into predicted dry biomass, with canopy volume as the sole predictor. Accuracy was assessed by plant-level five-fold cross-validation repeated with 100 random fold assignments, so every plant was predicted once by a model that had not been trained on it. UAV 3DGS with SuGaR had the highest mean performance, with R² of 0.85, RMSE of 9.32 grams and mean absolute percentage error of 16.02%. UAV SfM followed with R² of 0.83, RMSE of 9.91 grams and MAPE of 16.08%. Ground SfM achieved R² of 0.79, RMSE of 11.09 grams and MAPE of 16.68%, while UAV NeRF achieved R² of 0.77, RMSE of 11.07 grams and MAPE of 17.46%. A paired bootstrap analysis of all six method pairs found no RMSE difference that remained significant after Holm adjustment for six comparisons. The non-significant result did not demonstrate equivalence, and the confidence intervals left differences of roughly two grams around the observed estimates unexcluded.

Two plants had markedly greater biomass than the rest: one Felicity plant with approximately 115 grams of dry biomass and one Camarosa plant with approximately 145 grams. Only the Camarosa observation was influential under every method, with a Cook’s distance far above the study threshold, and its measured biomass exceeded the value predicted from canopy volume. The similarly large Felicity plant was predicted accurately and had low influence. The researchers proposed that denser foliage and more leaf and stem mass per unit of enclosed canopy volume might explain the Camarosa result, but foliage density was not measured and the explanation was based on a single plant. It remains a hypothesis rather than a demonstrated mechanism. More generally, the external convex-hull measurement does not directly capture genotype-dependent internal structure, which could contribute residual variation to the volume–biomass relationship.

Timing measurements showed that fast data acquisition did not yet translate into a fully automated high-throughput phenotyping pipeline. A UAV recorded a complete plot in about 36 seconds, while the total automated UAV SfM workflow required approximately 12.1 minutes per plot. UAV NeRF required 11.6 minutes, ground SfM 11 minutes, and UAV 3DGS with SuGaR 54.6 minutes. Destructive biomass sampling required about 10 minutes per plot, followed by five to seven days of drying. These comparisons excluded manual plant segmentation, point-cloud or mesh denoising, ground-control-point placement and platform setup. The demonstrated advantages of the UAV workflow were therefore rapid, non-destructive and repeatable acquisition, rather than complete end-to-end speed. Automating segmentation and marker placement, and reducing the computational burden of 3DGS with SuGaR, were identified as prerequisites for scaling the method to large trials.

The study has several limits that constrain generalization. It included only late-season plants from one experimental site, so performance at earlier growth stages, in other fields or seasons, or in other crops remains unknown. Earlier canopies may be less occluded but contain proportionally larger internal gaps, creating a different balance between reconstruction error and convex-hull inflation. Plants within a plot also share a reconstruction, ground-control-point registration and lighting conditions, so plant-level cross-validation may overstate performance for entirely new plots. Leave-one-plot-out validation gave similar results, but with only four plots those estimates remain imprecise. Preprocessing was not identical across methods: point clouds underwent hue filtering, whereas meshes were manually denoised, and operator repeatability was not assessed. The field experiment therefore supports the feasibility of non-destructive strawberry biomass estimation, while additional seasons, independent plots, common automated preprocessing and independently measured canopy volume are needed to establish how well the approach generalizes.

Subject of Research: Comparison of UAV and ground-based 3D reconstruction methods for estimating dry biomass in field-grown strawberries

Article Title: UAV-based 3D canopy reconstruction for non-destructive strawberry biomass estimation: Comparison of SfM, NeRF, and Gaussian Splatting

Article References: Shen, K., Wang, X., Contreras, A. B., Ji, L., Dalid, C., Zheng, C., Smith, K. E., Li, C., & Whitaker, V. (2026). UAV-based 3D canopy reconstruction for non-destructive strawberry biomass estimation: Comparison of SfM, NeRF, and Gaussian Splatting. Smart Agricultural Technology, 15, Article 102649. https://doi.org/10.1016/j.atech.2026.102649

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102649

Keywords: strawberry biomass, UAV imaging, 3D reconstruction, SfM photogrammetry, Neural Radiance Fields, 3D Gaussian Splatting, plant phenotyping, canopy volume, dry biomass, field phenotyping, UAV-based, canopy

The research, published in the journal Plant Methods by Radek Zenkl, Bruce A. McDonald, and Jonas Anderegg of the Plant Pathology Group at ETH Zürich, tackles a fundamental trade-off that arises whenever scientists point a high-resolution camera at a diseased wheat canopy. To capture the miniature diagnostic features of a pathogen—the small necrotic lesions of septoria tritici blotch or the rust-colored pustules of brown rust—a camera needs a narrow field of view and a shallow depth of field. That combination yields exquisitely precise measurements of disease on the handful of leaves in focus, but it means each image samples only a tiny sliver of the plot. And because disease is not spread evenly across a canopy, any single spot-level estimate can be wildly unrepresentative of the plot as a whole.

This is the precision–variance paradox at the heart of the study. A single high-resolution image delivers a measurement with almost no technical error, yet that measurement may carry enormous sampling error because it reflects only one small patch of foliage. The result is a paradox familiar to statisticians but often ignored by phenotyping engineers: perfect precision at the local scale can coexist with terrible reliability at the plot scale, which is the scale that actually matters for breeding decisions. A breeder comparing two wheat lines does not care about the disease level on one leaf; she cares about the average severity across an entire experimental plot, and the confidence she can place in that average.

To quantify this problem rigorously, the team assembled an unusually rich dataset. They acquired 16,650 high-resolution macro-scale images across 15 experimental plots over two consecutive days, photographing at multiple positions around each plot and at multiple heights within the canopy. The diseases under scrutiny were septoria tritici blotch, caused by the fungal pathogen Zymoseptoria tritici, and brown rust, caused by Puccinia species—two of the most economically important wheat diseases worldwide. Each image was analyzed to produce a spot-level estimate of disease severity, generating a dense grid of local measurements whose statistical properties could then be dissected.

The analysis focused on two key statistical properties of these local estimates: their distribution and their spatial dependence. Spatial autocorrelation—the tendency for nearby measurements to resemble each other more than distant ones—turns out to be the critical quantity. If two images taken a few centimeters apart provide essentially the same information, then photographing both adds far less value than photographing two widely separated spots. The researchers found that autocorrelation within a focal image stack, where multiple pictures are taken at slightly different focal depths of the same foliage, was comparable to the autocorrelation observed across different positions within a plot. In practical terms, the canopy’s disease heterogeneity operates at similar scales whether you move the camera laterally or shift its focal plane.

This finding yielded a concrete rule of thumb with immediate practical value. The team calculated that a stack of 10 images acquired with focus bracketing—the technique of automatically capturing a rapid burst of photos at successive focal depths and later merging them—contributed approximately 2.5 statistically independent observations. Strikingly, sampling at 10 separate positions around a plot delivered roughly the same effective sample size. In other words, a single camera station equipped with focus bracketing can substitute for a meaningful fraction of the walking effort that field technicians traditionally expend moving between positions. Because focus bracketing is automated on many modern cameras, it increases the sampled leaf area without any additional operator time, making it an unusually cheap precision gain.

But raw averaging of local estimates is not enough, and this is where the study makes its most sophisticated contribution. The authors modeled plot-level severity as a latent Beta-distributed variable. The Beta distribution is the natural statistical home for quantities bounded between zero and one, such as the proportion of leaf area affected by disease. By treating the true plot-level severity as a latent—meaning unobserved but inferable—parameter, and the spot-level image estimates as noisy draws from a Beta distribution centered on that parameter, the researchers could estimate both the mean disease severity and its associated uncertainty in a statistically coherent way. This hierarchical framing explicitly acknowledges that each image is a sample, not a census, and propagates sampling uncertainty into the final plot-level estimate.

The Beta-model results carried an important warning for anyone timing their phenotyping campaigns. The value of dense spatial sampling was not constant across the epidemic; it became particularly important at advanced stages of disease development. Early in an epidemic, when lesions are sparse and scattered, local estimates may be relatively homogeneous. But as the canopy fills with lesions and necrotic tissue, the spatial structure of disease becomes more complex and the variance among spot-level estimates grows. At these late stages, a sparse sampling strategy risks producing plot-level averages that are precise-looking but wrong, while dense sampling combined with an appropriate uncertainty model keeps the estimates honest. For breeders who often score plots near the end of an epidemic to maximize differentiation between resistant and susceptible lines, this timing effect is directly relevant.

The authors are careful about the limits of what their experiment can claim. With 15 plots and two days of imaging, the study was designed to characterize the correlation structure and demonstrate the modeling framework rather than to deliver definitive precision benchmarks. Obtaining a fully precise understanding of the autocorrelation structure, and an accurate quantification of exactly how much precision can be gained from any given number of repeated measures, will require larger experiments spanning more plots, more genotypes, and more epidemic scenarios. The effective sample size figures—2.5 independent observations per 10-image stack or per 10 positions—are best read as an initial calibration for this imaging configuration rather than a universal constant, since autocorrelation will inevitably vary with disease type, canopy architecture, and growth stage.

Even so, the practical message is clear and potentially transformative for field phenotyping. Combining dense spatial sampling with focus bracketing and hierarchical statistical models can substantially improve the precision of plot-level disease estimates without sacrificing throughput—the perennial bottleneck that has kept high-resolution imaging out of large breeding trials. The workflow requires no exotic hardware: a consumer-grade camera with a macro lens and automated focus bracketing, a disciplined multi-position sampling protocol, and an analysis pipeline that treats images as samples from a Beta-distributed latent variable rather than as independent ground truth. As breeding programs worldwide race to develop wheat varieties resilient to septoria tritici blotch and rust under shifting climates, the reliability of the severity numbers feeding those decisions becomes a first-order concern. This study shows that the gap between what a camera can see and what a breeder needs to know can be bridged—not by better lenses alone, but by better statistics applied to the images we already know how to take.

Subject of Research: High-resolution image-based phenotyping for estimating plot-level wheat disease severity

Article Title: A novel phenotyping approach for reconciling precision and variance in disease severity estimates from high-resolution imaging

Article References: Zenkl, R., McDonald, B. A., & Anderegg, J. (2026). A novel phenotyping approach for reconciling precision and variance in disease severity estimates from high-resolution imaging. Plant Methods. https://doi.org/10.1186/s13007-026-01593-8

Image Credits: AI Generated

DOI: 10.1186/s13007-026-01593-8

Keywords: field phenotyping, disease severity, septoria tritici blotch, brown rust, focus bracketing, spatial autocorrelation, Beta distribution, wheat breeding, plant pathology, image analysis, precision agriculture, statistical modeling