Scientists in Brazil have demonstrated that a combination of three non-destructive imaging techniques, visible-spectrum (RGB) photography, X-ray radiography, and three-dimensional magnetic resonance imaging (MRI), can detect and characterize hidden damage in soybean seeds that conventional quality tests frequently miss. The study, published in Smart Agricultural Technology, comes at a critical moment: Brazil is the world’s largest soybean producer and exporter, with an estimated harvest of 166 million tons for the 2024/2025 growing season, and seed quality is increasingly under threat from the very climate extremes that help drive the country’s agricultural productivity.

The research, led by Rafael Mateus Alves and colleagues at institutions including the Seed Technology Center of Embrapa Soybean and the University of São Paulo, was funded by the São Paulo Research Foundation (FAPESP). Its central premise is deceptively simple: because seed quality is largely determined in the field, before harvest even begins, researchers and seed companies need tools that can look inside a seed without killing it. Destructive assays such as the tetrazolium viability test, which stains metabolically active tissue red, are the industry standard, but they end the life of the sample. That is a particular problem for breeding programs, where each seed represents a unique genetic combination that must survive analysis if it is to be planted.

The seed itself is a small miracle of engineering. A protective coat, derived from the ovule integuments and marked by a scar called the hilum, regulates water uptake and shields the embryo within. The embryo contains the plumule, which gives rise to the plant’s first true leaves; the apical meristem, precursor of the aerial plant; and the hypocotyl–radicle axis, which breaks through the soil and forms the primary root. Flanking this axis are the cotyledons, storage organs packed with macromolecules that are metabolized and shipped to the growing axis during germination. Damage to any of these structures, from insects, fungi, drought, heat, or rough handling during harvest, can silently reduce a seed lot’s vigor, the capacity for rapid, uniform germination that underpins successful crop establishment.

To interrogate this architecture non-invasively, the team assembled ten representative seeds from production fields, commercial lots, and storage samples, each embodying a distinct damage category: stink bug injury, purple seed stain caused by fungi, green seeds from incomplete maturation, wrinkled seeds from heat and drought stress, weathering-related coat ruptures, severe structural failure, mechanical harvest damage, and internal galleries bored by the cigarette beetle (Lasioderma serricorne) during storage. Every seed was imaged sequentially with each of the three modalities and then subjected to the tetrazolium test as a qualitative reference standard.

The technical pipeline was meticulously standardized. For visible-light imaging, seeds were mounted on acetate sheets and photographed with a Leica DMLB microscope equipped with a DFC 310FX digital camera, oriented in four standardized positions with the hilum consistently to the right. X-ray radiographs were acquired on a Faxitron MX-20 DC-12 system operating at 34 kilovolts, with a nine-second exposure and an object-to-source distance of 11.4 centimeters; uniform contrast settings were applied to all images to make damaged tissues directly comparable. The MRI work used a horizontal 2 Tesla superconducting magnet coupled to a Bruker Avance III spectrometer operating at 85.24 megahertz for proton nuclei. Before scanning, seeds were conditioned to raise their moisture content from roughly 8–10 percent (wet basis) by incubation on moistened germination paper for three hours at 25 °C, a step that ensured sufficient hydrogen signal for imaging. A three-dimensional FLASH pulse sequence, with a 3-millisecond echo time and 75-millisecond repetition time, collected data on a 128×128×128 matrix over a 12.8-millimeter field of view, yielding isotropic voxels of 100 micrometers per side. Datasets were zero-padded to 256³ before Fourier transformation and rendered in three dimensions using ImageJ with the 3Dscript plugin.

The resulting image galleries are striking. On seeds punctured by stink bugs, the dominant pest of Brazilian soybean fields, RGB images revealed darkened patches on the cotyledons and near the plumule, the visible legacy of insect feeding that lacerates tissue and injects yeasts that degrade it from within. X-ray images flagged zones of reduced radiation absorption in the same darkened regions, while MRI cross-sections exposed the depth of tissue destruction with unusual clarity, precisely because degraded tissue no longer absorbs water and therefore produces no magnetic resonance signal. The tetrazolium test confirmed that these zones lacked respiratory activity, meaning they were metabolically dead.

Not every symptom tells the same story, however, and the multimodal approach proved especially powerful at distinguishing superficial from structural damage. Seeds with purple seed stain, caused by Cercospora fungi that secrete the toxin cercosporin, showed internal structures that appeared entirely normal in both X-ray and MRI. Yet the tetrazolium test produced an unusually intense red coloration at the stained coat, indicating that fungal activity had compromised the seed coat’s ability to regulate water uptake, allowing the staining solution to penetrate more deeply into the embryo. Similarly, green seeds, which retain chlorophyll because heat and drought interrupted maturation, showed no structural anomalies under X-ray or MRI, but their membranes proved unusually permeable to the tetrazolium solution, hinting at the physiological deterioration typical of immature tissue.

Where MRI truly distinguished itself was in detecting damage invisible to every other method. One seed that appeared flawless in visible light and radiographically normal in X-ray images revealed, in its three-dimensional MRI reconstruction, internal ruptures running through the central cotyledons, the likely consequence of rapid, uneven drying that forces differential cellular expansion and eventual tissue collapse. In mechanically damaged seeds, the X-ray images showed a crack in the hypocotyl–radicle axis that the camera could not see, but MRI went further, revealing the full extent of the fissure and the displacement of the axis relative to the cotyledons. The team also documented a dramatic structural failure nicknamed the “popcorn effect,” in which alternating wetting and rapid drying cycles tear the seed coat and force the cotyledons apart, a phenomenon increasingly reported in Brazilian seed lots as late-season weather grows more erratic.

To move beyond qualitative observation, the researchers extracted grayscale histograms from each X-ray image, normalizing pixel frequencies across the full 0–255 intensity range. Two statistical descriptors, mean gray level and Shannon entropy, were then compared across damage categories using one-way ANOVA followed by Tukey’s honest significance test. The analysis yielded clear separation: undamaged seeds showed the narrowest distributions with peaks at high grayscale values, while beetle-infested seeds and wrinkled seeds displayed the broadest distributions with heavy weight in the lower gray ranges, reflecting hollowed-out tissues and sparse, deformed structures. Mean gray level differed significantly across categories, peaking in undamaged seeds and bottoming out in wrinkled ones, while Shannon entropy, a measure of histogram complexity, was highest in beetle-damaged seeds and lowest in healthy ones. Because each damage category produced a characteristic radiographic fingerprint, the authors suggest these histogram metrics could support automated, quantitative screening in commercial seed laboratories.

Each modality, the authors stress, occupies a distinct niche. RGB imaging is fast, cheap, and ideal for surface screening but blind to anything beneath the coat. X-ray radiography, already a mainstay of seed testing labs, reveals internal voids, malformations, and cavities rapidly and non-destructively, though it cannot report on metabolic viability and demands careful standardization. MRI, with its superb soft-tissue contrast and genuine three-dimensional resolution, excels at detecting subtle physiological and structural alterations, latent mechanical injury, hydration-induced ruptures, and moisture redistribution, but its cost and acquisition time currently confine it to research settings. The tetrazolium test remains valuable as a reference but is destructive and subject to interpreter bias.

The authors argue the techniques should be viewed as complementary rather than competing, and they sketch a future in which RGB and X-ray screening handle high-throughput commercial quality control while MRI resolves ambiguous or high-stakes cases, such as breeding lines and seed physiology studies. Advances in imaging hardware, automation, and artificial intelligence-based analysis, they note, could eventually fuse these modalities into scalable phenotyping workflows. For now, the message is clear: in an era when a single season of heat, drought, and hungry insects can undermine an entire seed lot, the ability to see damage in all three dimensions, without sacrificing the seed itself, may prove as transformative for agriculture as it has been for medicine.

Every spring, Europe’s forests are showered with far more than rain. Wind-pollinated trees release staggering quantities of pollen into the atmosphere, and much of it eventually settles on leaves, branches and the forest floor. New research now shows that this pollen is not merely biological debris — it is a measurable, taxon-specific driver of nutrient fluxes in the water that drips through forest canopies, a flow scientists call throughfall. The findings, published in Environmental Monitoring and Assessment, provide the first continental-scale evidence directly linking pollen deposition to forest water chemistry, and they suggest that decades of forest monitoring may have been quietly misattributing a slice of nutrient input to other sources.

The study was carried out by a large international team led by Elena Gottardini of the Fondazione Edmund Mach in Italy, working within the ICP Forests programme, a pan-European network that monitors air pollution effects on forests. The researchers analysed throughfall samples collected in 2018 from 60 intensive monitoring plots — known as Level II plots — across eight countries: Belgium, Finland, France, Germany, Italy, Norway, Switzerland and the United Kingdom. The plots ranged in altitude from 14 to 1,900 metres above sea level and were dominated by six tree genera: Abies, Fagus, Larix, Picea, Pinus and Quercus. In total, 196 throughfall samples were collected between February and July, covering the early vegetative season when most European trees flower.

The innovation of the study lies in its methodology. Rather than inferring pollen inputs from nearby aerobiological samplers, the team examined the very same water samples that were being chemically analysed. A known quantity of water from each sample was filtered through a 5-micrometre cellulose nitrate mesh, and a calibrated number of 10-micrometre polystyrene-divinylbenzene microspheres — roughly 155,000 per sample — were added as a counting standard. The filters were then dissolved in acetone, centrifuged, and the resulting pellet embedded in fuchsin jelly on microscope slides. Trained analysts identified and counted pollen grains under 400-times magnification, stopping when about 1 percent of the added microspheres had been counted. From these counts, the team calculated pollen deposition rates in grains per square metre per day, correcting for filtered volume, throughfall quantity and sampling duration.

The results were striking. Fifty-three pollen taxa were identified across the samples, with woody species accounting for 98 percent of all grains. Four genera — Pinus, Picea, Fagus and Quercus — together made up 91.4 percent of the total. Pine pollen alone represented more than half of everything collected, a dominance the researchers attribute to the buoyant air sacs on pine grains, which allow them to travel enormous distances from both local and distant sources. In beech, spruce and pine stands, the pollen of the dominant genus itself prevailed, making up 49.7, 59.1 and 84.3 percent of the local pollen spectrum respectively. In oak stands, oak pollen ranked second at 26.9 percent, behind pine. Data from fir and larch stands were excluded because sampling there did not align with those genera’s flowering periods.

By combining the measured pollen curves with published flowering phenology, the team defined main pollen seasons for each genus. Beech and oak flowering spanned April and May, with beech peaking in the last week of April and oak showing a double peak at weeks 19 and 23, likely reflecting multiple oak species flowering at slightly different times across the wide latitudinal and altitudinal spread of plots. Spruce pollen peaked around weeks 19 to 20, while pine peaked at week 19 with a secondary pulse at week 22. Overall, 57 percent of the throughfall samples fell within their stand’s flowering period for at least half of their collection window, providing a robust temporal overlap between pollen deposition and water chemistry measurements.

The chemical analysis, conducted at the Research Institute for Nature and Forest laboratory in Belgium, quantified major ions including potassium, calcium, magnesium, sodium, chloride, phosphate, sulphate, nitrate, nitrite and ammonium using ion chromatography, alongside total dissolved nitrogen and non-purgeable organic carbon measured on a TOC/TN analyser. Linear mixed-effects models then tested whether pollen deposition rates could predict the flux of these compounds, with plot identity included as a random effect and autocorrelation between repeated samples explicitly modelled.

The answer was a clear yes. Every statistically significant association between pollen and throughfall chemistry was positive, meaning more pollen corresponded to more nutrients reaching the forest floor. Potassium emerged as the star player: its flux was significantly explained by pollen from all four main genera, with pollen accounting for an average of 33 percent of the variance. This makes biochemical sense, because potassium is one of the predominant ions in pollen grains, and pollen washed from flowers and crowns by rain or deposited dry is a direct source of the ion during the flowering season — an idea first proposed decades ago but never confirmed at this scale. The strongest single relationships involved oak pollen, which explained 77.8 percent of the variance in ammonium fluxes and 55 percent in total dissolved nitrogen.

Broadleaved and coniferous pollen left different chemical fingerprints. Beech and oak pollen together significantly explained fluxes of non-purgeable organic carbon, dissolved organic nitrogen, total dissolved nitrogen, ammonium and magnesium, while the combined pollen of pine and spruce was linked with every measured compound except phosphate. Intriguingly, broadleaved pollen showed a weak negative association with nitrate fluxes. The authors urge caution in interpreting this pattern, since several non-exclusive mechanisms could be at play. Laboratory experiments have shown that pollen and its associated microbiota can transform extracellular nitrate into nitrite and nitrous oxide, an effect pronounced in beech and oak but weak or absent in pine and spruce. Canopy uptake of nitrogen and coincidentally lower atmospheric deposition during broadleaf flowering are also possible explanations that this single-year study cannot disentangle.

The dissolved organic carbon carried in throughfall also differed by forest type. Specific ultraviolet absorbance at 254 nanometres, a proxy for aromatic and recalcitrant carbon, was highest in spruce stands, whose needles release lignin derivatives and tannins. The humification index was significantly higher in oak throughfall, reflecting more decomposed, humic organic matter. Most revealing was the fluorescence index, which distinguishes microbial from plant-derived organic matter: beech and oak samples had significantly higher values, with medians near 1.9, than pine and spruce samples, which sat near 1.7. Values above 1.9 typically indicate microbially processed carbon, and the elevated readings under broadleaves suggest microbes play a larger role in transforming pollen-derived carbon in those forests — hinting that pollen inputs feed not just trees but entire soil microbial food webs.

For the authors, the implications reach well beyond academic curiosity. Routine throughfall monitoring has historically ignored pollen as a variable, meaning that chemical enrichment previously attributed solely to canopy leaching or atmospheric deposition may in fact be partly pollenic. As climate change pushes flowering earlier and increases airborne pollen amounts across arboreal taxa, these hidden inputs could grow. Masting behaviour in beech and oak — the episodic, synchronized production of enormous seed and pollen crops — introduces strong year-to-year variability that any nutrient budget must now reckon with.

The researchers acknowledge the limitations of a one-year, correlative dataset and call for multi-year studies incorporating canopy architecture, stand age, phenology and insect infestations, as well as stable isotope tracing or controlled exclusion experiments to nail down mechanisms. Still, the message is clear: pollen is not just about reproduction, or even allergies. It is a seasonal fertiliser delivered from the sky, and forests have been banking on it all along. Incorporating pollen surveys into long-term forest observation networks, the team argues, will be essential for accurate nutrient budgets in a changing climate.