Herbarium records lead Bucknell researcher to a new plant species in the Australian outback
Specialized organs for feeding ants are first of their kind
Pensoft Publishers
image:
Morphology of Solanum nectarifolium, a newly-described species of Australian bush tomato.
view moreCredit: Kym Brennan
LEWISBURG, Pa. — A recent study led by Bucknell University Professor Chris Martine, biology, the David Burpee Professor in Plant Genetics & Research, has identified and described a new species of bush tomato with a special connection to ants — a taxonomic journey sparked by unusual specimens held in Australian herbarium collections.
The study, co-authored by a set of Australian botanists and Jason Cantley — the former Burpee Postdoctoral Fellow in Botany at Bucknell who is now Associate Professor of Biology at San Francisco State University — was published in the open-access journal PhytoKeys and underscores the critical role that natural history collections play in biodiversity science. The new species, Solanum nectarifolium, or the Tanami Bush Tomato, was named for the location of its original collection area — the northern edge of the Tanami Desert — and for the uniquely conspicuous nectar-producing organs on the undersides of its leaves. These extrafloral nectaries exude a sweet liquid to attract ants that might protect the plant from herbivores. This remarkable trait marks the first known Solanum species with extrafloral nectaries visible to the naked eye, a feature previously observed only microscopically in a handful of related Australian species.
Martine first had an inkling that something was unusual about the plants from that region of the Northern Territory while working on a project with another former Burpee Postdoc, Angela McDonnell, now an Assistant Professor at St. Cloud State University. The pair included DNA extracted from two herbarium specimens representing Solanum ossicruentum, a species known as the Blood Bone Tomato that the Martine Lab described in the same journal in 2016, in an ongoing analysis meant to build a new bush tomato evolutionary tree.
“We couldn’t understand why the two collections of the same species kept showing up in different parts of the tree,” says Martine. “I had collected one of them and was certain that it represented Solanum ossicruentum, so I reached out to the person who collected the other one, David Albrecht, and asked whether he thought the plants he saw in 1996 at a place called Jellabra Rockhole could be something else.”
Albrecht, Senior Botanist at the Northern Territory Herbarium at Alice Springs, suggested that the best way to know would be for botanists to revisit that remote region of the northwestern Tanami Desert and see for themselves. Martine, who had participated in seven collecting expeditions to northern Australia since 2004, wasn’t disappointed.
“I was kind of hoping he’d tell me that,” Martine says. “Because I was already planning some new fieldwork in the Northern Territory and this would give me a great season to visit an area I had never been to before. But to really be prepared for a trip like that, I first needed to understand what other botanists had recorded and collected there in the past – and there is only one surefire way to do that: check what is in the herbarium collections.”
So Martine started by using the Australasian Virtual Herbarium (AVH), a database of every plant specimen held in every herbarium in Australia. He searched for collections made of Solanum ossicruentum and a similar species called Solanum dioicum in the northern Tanami, finding 15 records for specimens gathered as far back as 1971.
“It was a really interesting distribution of points on the map, too,” Martine says. “These were far south of the other records for Solanum ossicruentum, with hundreds of miles of ‘empty’ country between the two clusters. I couldn’t wait to get to Australia to see what those Tanami plants looked like.”
In May 2025 Martine headed to Australia to meet his team for the trip: Cantley and paper coauthors Kym Brennan, Aiden Webb, and Geoff Newton, all associated with the Northern Territory Herbarium at Palmerston. But, first, Martine made a stop in another plant collection in the southwestern city of Perth.
“The visit to the Western Australian Herbarium was my first chance to spend a bunch of time with some of the actual specimens that I had earmarked based on the data in AVH,” Martine explains. “And what I saw there legit blew my mind.”
Every specimen looked similar to Solanum ossicruentum, except for a few subtle characteristics – and one thing that Martine had never seen in more than two decades of Outback botanizing.
“On the backs of the leaves, along the veins, were these visible round disks,” Martine notes. “They were each around a half-millimeter wide, really obvious, and the only bush tomato specimens that had them – we’re talking hundreds and hundreds of collections – were the ones from the northern Tanami.”
Martine thought they could be extrafloral nectaries (EFNs), non-flower organs on a plant that exude sweet liquid, typically as a means to attract ants that might protect the plants from herbivores. These were known to exist in a few Australian bush tomatoes, but those are tiny and have only been confirmed with microscopes. EFNs that could be seen without magnification would be something truly novel.
A few days later, Martine was in the herbarium at Palmerston and found the same pattern: more visible disks and only on plants from that same geographic area. Then he noticed that the most recent collection, from 2021, had been made by Kym Brennan – a renowned field biologist with an expertise in photography who was preparing for their trip in the next room.
“I ran in there and asked whether he remembered anything unusual about that collection – and before I could finish my explanation for why, he was already showing me an incredible photo of the leaves of that same plant. They were positively oozing with shiny, round droplets of nectar. And all from those disks on the veins.”
Eight days and more than 1000 kilometers of driving later the team arrived near Brennan’s collection site 50 kilometers southwest of the community of Lajamanu, right along the edge of the unpaved Lajamanu Road.
“This was more-or-less the same place where others had collected it in the early 1970s, so we were cautiously optimistic that we’d not only find it there again, but that the plants would have the flowers and fruits on them that we needed to describe this as a new species,” explains Martine. “But it’s a harsh environment and the abundance of bush tomatoes is often dependent on fire occurrence. Sometimes you get to a place and there is nothing but old gray stems. Other times there are more happy plants than you can count. In this case, it was the latter situation!”
The team got to work taking notes, making measurements, and shooting photographs. And then Cantley called for Martine to come over to the plant he was examining. There were ants all over the leaf undersides, avidly moving from disk to disk and probing them for nectar. Hypothesis confirmed.
The collaborators decided on the scientific name “nectarifolium” – which translates to “nectar leaf,” for obvious reasons – and the English-language name Tanami Bush Tomato. Martine then contacted a few experts about the conspicuous nature of the EFNs and whether that has been seen anywhere else in the genus Solanum, a group of around 1200 species that includes the tomato, potato, and eggplant.
“As far as we know, this is the first Solanum species to be described as having extrafloral nectaries that you can see with your naked eye. That’s a pretty cool finding – and it all started with the examination of specimens that have been waiting in herbaria for as long as a half-century for someone to come along and take a closer look.”
Bucknell’s own Wayne E. Manning Herbarium, which holds approximately 25,000 plant specimens, now includes new samples of the Tanami Bush Tomato. But the official holotype remains at the Northern Territory Herbarium in Palmerston — almost 10,000 miles away from Bucknell’s campus.
“The Manning Herbarium may be small, but every specimen is a snapshot of biodiversity,” Martine says. “These collections allow us to study where species occur, how they’ve changed over time, and — in cases like this — even help discover new ones.”
The publication of the new species comes amid broader concern over the fate of natural history collections, such as Duke University’s recently announced closure of its herbarium housing more than 800,000 specimens. Martine and his colleagues agree that such closures could hinder future discoveries and conservation efforts.
Martine, a leading expert on Australian bush tomatoes, was recently elected president-elect of the Botanical Society of America. He will begin his term as president following the organization’s annual meeting in August 2026.
“It still doesn’t feel real and probably won’t until I start my term just after Botany 2026,” Martine says. “But I promise to do my best because plants are awesome and so are botanists.”
Original study:
Martine, C.T., Brennan, K., Cantley, J.T., Webb, A.T. and Newton, G. (2025). A new dioecious bush tomato, Solanum nectarifolium (Solanaceae), from the northern Tanami Desert, Northern Territory, Australia, with reassessment of S. ossicruentum and a change in the circumscription of S. dioicum. PhytoKeys, 268, pp.183–199. doi: https://doi.org/10.3897/phytokeys.268.169893
Immature fruit and fruiting calyx of Solanum nectarifolium, a newly-described species of Australian bush tomato.
Extrafloral nectaries (EFNs) on the leaves of Solanum nectarifolium.
Staminate flowers of Solanum nectarifolium, a newly-described species of Australian bush tomato.
Credit
Kym Brennan
Journal
PhytoKeys
Method of Research
Survey
Subject of Research
Not applicable
Article Title
A new dioecious bush tomato, Solanum nectarifolium (Solanaceae), from the northern Tanami Desert, Northern Territory, Australia, with reassessment of S. ossicruentum and a change in the circumscription of S. dioicum
How a fungus leads to tissue growths in maize
A University of Bonn study has shown how a maize pest is hijacking the plant’s root-building function
University of Bonn
image:
Tissue structure alterations caused by infection are visible under a microscope (center). When Arabidopsis thaliana (right) forms the fungal effector protein Tip4, gall-like accumulations of undifferentiated cells appear instead of lateral roots.
view moreCredit: Figure: (c) Professor Djamei’s research group / University of Bonn
When a maize plant is attacked by the fungus Ustilago maydis, tumor-like tissue growths occur at the site of infection. How the pathogen causes this response in its host has long been unknown. But a University of Bonn study has now shown how the fungus takes over the plant’s function for forming lateral roots. The findings have been published in the journal New Phytologist.
Ustilago maydis attacks the leaves of the maize plant. Galls conspicuously form at the infection site, which may be as large as a child’s head. The harmful fungus benefits from this response as the massive tissue growth diverts energy and resources which are then unavailable to defend against the pathogen. Ustilago also benefits from an ideal nutrient supply found in the tissue, from which it can flourish.
“For a long time, it was not properly understood how the fungus causes its host to form galls,” says Professor Armin Djamei, head of the Department of Plant Pathology of the Institute of Crop Science and Resource Conservation (INRES) at the University of Bonn, whose research group is working to discover the mechanism. “We knew that Ustilago produces hundreds of proteins that manipulate the maize. ‘Tip effectors’ are one type of these proteins.”
Fungal genes inserted into the plant genome
The researchers inserted the fungal genes containing the instructions for forming tip effectors into the genome of the model plant Arabidopsis thaliana. The plant responded by producing proteins that are normally produced by the fungus, which made it possible to find out the precise impact of these molecules.
“Our genetically modified plants exhibited characteristic abnormalities in their roots,” relates Dr. Mamoona Khan, who conducted the experiments in large part. “There they formed ‘calli,’ i.e. tissue with fast-multiplying cells. The calli consist of plant stem cells which under normal circumstances are activated during the process of lateral root formation, so that they start to divide.” By conducting genetic experiments on maize and further analyses, the researchers were able to show that this discovery is very likely relevant to the natural host of Ustilago maydis.
Leaf pest hijacks root formation
Ustilago appears to hijack the lateral root formation function in order to accelerate cell division activity in maize leaves, leading to the formation of galls. This hypothesis is supported by additional findings by the team that tip effectors regulate the formation of various transcription factors, which help determine what genes are expressed and in what quantity.
The transcription factors that have to be upregulated for lateral roots to form are known. The researchers altered these factors in maize plants so that they no longer functioned. When infected by Ustilago, those plants only developed very small galls. “We also did a comparison against the genes that are active in gall formation and lateral root development in normal maize plants,” Khan elaborates, “and found significant similarities that cannot be merely coincidental.”
Valuable insights for breeding more resistant varieties
Ustilago maydis produces no toxins, so infected corn is still usable as animal feed without problem. The pathogen thus does not pose a major agricultural problem. In certain related species however, the situation differs. “Among the smut fungi, which include Ustilago, there are a few important pests,” points out Professor Armin Djamei, who is also active in the Sustainable Futures Transdisciplinary Research Area (TRA) and in the PhenoRob Cluster of Excellence at the University of Bonn. “Obtaining a better understanding of infection mechanisms could potentially allow breeding crop varieties that are resistant to those pathogens.”
Institutions involved and funding secured:
Funding for the study was provided by the European Research Council (ERC), the Austrian Science Fund (FWF), the Austrian Academy of Sciences and the German Research Foundation (DFG).
Journal
New Phytologist
Article Title
Pathogenic fungus Ustilago maydis exploits the lateral root regulators to induce pluripotency in maize shoots
New "Stomata in-sight" system allows scientists to watch plants breathe in real-time
image:
Representative 16-bit confocal microscope image of an open Zea mays stoma.
view moreCredit: Plant Physiology, Volume 199, Issue 4, December 2025, kiaf600, https://doi.org/10.1093/plphys/kiaf600
URBANA, Ill. — For centuries, scientists have known that plants "breathe" through microscopic pores on their leaves called stomata. These tiny valves are the gatekeepers that balance the intake of carbon dioxide into the leaf for photosynthesis against the loss of water vapor from the leaf to the atmosphere. Now, researchers at the University of Illinois Urbana-Champaign have developed a groundbreaking new tool that allows them to watch and quantify this process in real-time and under strictly controlled environmental conditions.
The study, published in the journal Plant Physiology, introduces a system dubbed "Stomata In-Sight." It solves a long-standing technical challenge in plant biology: how to observe the microscopic movements of stomatal pores while simultaneously measuring how much gas they are exchanging with the atmosphere.
The "Mouths" of the Plant, stomata (Greek for "mouths"), are critical to global agriculture. When they open, plants get the carbon they need to grow, but they also lose water. Therefore, understanding how the number and operation of these pores determine the efficiency of photosynthetic gas exchange is key to developing crops that need less water to grow and can reliably produce food, biofuel and bioproducts in times and places of drought stress.
"Traditionally, we've had to choose between seeing the stomata or measuring their function," explained the research team. Previous methods often involved making impressions of leaves (like taking a dental mold), which only captures a static snapshot, or using standard microscopes that observe the leaf without being able to control the conditions the leaf is experiencing. This is important because the stomata are highly responsive to variation in almost all aspects of the environment.
A Window into the Leaf The new "Stomata In-Sight" system integrates three complex technologies into one:
1. Live Confocal Microscopy: A powerful imaging technique that uses lasers to create detailed, three-dimensional views of living cells without slicing into the plant.
2. Leaf Gas Exchange: High-precision sensors that measure exactly how much CO2 the leaf is taking in and how much water it is releasing.
3. Environmental Control: A chamber that allows researchers to manipulate light, temperature, humidity, and carbon dioxide levels to mimic real-world conditions.
By combining these, the team can watch exactly how the stomata respond to variation in the environment.
Why It Matters This high-definition view of plant physiology could revolutionize how we breed crops. By understanding the precise mechanical and chemical signals that cause stomata to open or close, and how that is influenced by the number of stomata on a leaf, scientists can identify genetic traits that lead to "smarter" plants—crops that use water most efficiently. That is crucial because water is the environmental factor that limits agricultural production the most.
The system was developed by Joseph D. Crawford, Dustin Mayfield-Jones, Glenn A. Fried, Nicolas Hernandez, and Andrew D.B. Leakey at the Department of Plant Biology and the Institute for Genomic Biology at the University of Illinois.
About the Paper The work was supported by the U.S. Department of Energy's Center for Advanced Bioenergy and Bioproducts Innovation, the National Science Foundation, and a philanthropic gift, and is published as an open-access article titled, "Stomata In-Sight: Integrating Live Confocal Microscopy with Leaf Gas Exchange and Environmental Control," in Plant Physiology. https://doi.org/10.1093/plphys/kiaf600
Contact: Andrew Leakey, leakey@illlinois.edu
Journal
PLANT PHYSIOLOGY
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Stomata in-sight: Integrating live confocal microscopy with leaf gas exchange and environmental control
From time series to a single snapshot: A smarter way to track wheat growth in real time
image:
Technical framework.
view moreCredit: The authors
By transferring temporal knowledge from complex time-series models to a compact model through knowledge distillation and attention mechanisms, the approach achieves high accuracy while greatly reducing data and computational demands. This enables real-time, field-ready wheat phenology monitoring suitable for practical agricultural deployment.
Traditional wheat phenology monitoring relies heavily on manual field observation, which is labor-intensive, subjective, and unsuitable for large-scale or continuous monitoring. Vegetation indices derived from RGB or multispectral imagery offer partial automation but struggle to distinguish visually similar growth stages and often require expert calibration and long time-series data. Deep learning has improved automation and accuracy by extracting rich visual features directly from images, yet most single-image models fail to capture the dynamic nature of crop growth. Multi-temporal deep learning models address this limitation but introduce new challenges, including large model size, high energy consumption, complex data pipelines, and poor real-time performance—especially on resource-constrained edge devices. These trade-offs have limited their practical adoption in everyday farming.
A study (DOI: 10.1016/j.plaphe.2025.100144) published in Plant Phenomics on 4 December 2025 by Xiaohu Zhang’s team, Nanjing Agricultural University, enables efficient, real-time wheat phenology detection suitable for practical field deployment.
The study adopted a framework to evaluate a lightweight wheat phenology detection model optimized for single-temporal images through knowledge distillation and multi-layer attention transfer. Model training and evaluation were conducted on a high-performance computing server equipped with dual Intel Xeon CPUs, seven NVIDIA Tesla A100 GPUs, and large-memory support, ensuring stable and efficient deep learning optimization. The backpropagation algorithm was used for parameter learning, with the Adam optimizer selected to balance convergence speed and model performance, while dropout regularization was introduced to reduce overfitting and enhance generalization. Training was performed using a batch size of 16, a learning rate of 0.0001, and a dropout rate of 0.3. Model performance was comprehensively assessed using multiple complementary metrics, including confusion matrices to analyze class-level predictions, overall accuracy (OA), F1-score, kappa coefficient, and mean absolute error (MAE), enabling a robust evaluation of both classification accuracy and consistency across phenological stages. Based on this methodology, the proposed model achieved strong performance, with an OA of 0.927, MAE of 0.075, F1-score of 0.929, and kappa coefficient of 0.916, demonstrating accuracy comparable to complex multi-temporal models despite using only single images. When benchmarked against widely used deep learning architectures such as ResNet50, MobileNetV3, EfficientNetV2, RepVGG, SCNet, STViT, and PhenoNet under identical training conditions, the proposed method consistently outperformed all comparators, achieving accuracy gains ranging from 2.5% to 17.5%. Notably, the lightweight student model exhibited only a 0.8% reduction in accuracy relative to its multi-temporal teacher model, while substantially reducing computational cost. Confusion matrix analysis showed a pronounced diagonal structure, indicating reduced misclassification across eight reproductive stages, particularly for visually ambiguous middle stages such as jointing, booting, anthesis, and flowering. Furthermore, evaluation on an unseen second-year dataset confirmed strong generalization ability, with an OA of 0.917 and stable performance across varying lighting conditions, wheat varieties, and field scenes, underscoring the model’s robustness and suitability for real-time agricultural deployment.
By requiring only a single image for inference, the proposed model dramatically reduces data storage needs, computational cost, and inference time. The lightweight student model processes images at real-time speeds suitable for on-farm deployment, including integration with field cameras, drones, or low-power edge devices. This capability makes accurate wheat phenology monitoring accessible to smallholder farmers and large-scale operations alike, without dependence on continuous image collection or auxiliary data such as weather records.
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References
DOI
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100144
Funding information
This research was supported by the National Key Research and Development Program of China (2024YFD2301100), the National Natural Science Foundation of China (Grant No. 32171892), the Qing Lan Project of Jiangsu Universities, and Jiangsu Agricultural Science and Technology Innovation Fund (CX (21) 1006).
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
Journal
Plant Phenomics
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
WPDSI: A deep learning method for wheat phenology detection from single-temporal images
Seeing roots push deeper: RootXplorer automates high-throughput phenotyping for soil-compaction resilience
Nanjing Agricultural University The Academy of Science
image:
RootXplorer is a computer vision-based phenotyping platform designed for high-throughput 3D imaging and quantification of root penetration traits/phenotypes in multiple plant species. RootXplorer integrates three main components: A) A Phytagel cylinder system (CS) for growing plant roots under artificial soil compaction conditions. Bottom Phytagel layer, BPL; upper Phytagel layer, UPL. B) A root architecture 3D imaging cylinder (RADICYL) system to scan plants in the CS and create 360-degree images or videos of roots in 72 frames. C) An image analysis pipeline (IAP) for automatic image cropping, segmentation and trait extraction. Image segmentation was conducted by deep learning models trained with a U-Net++ convolutional neural network (CNN) architecture. After segmentation, root penetration traits for each cylinder/plant are extracted via computer vision and used to calculate penetration phenotypes. Upper root count, URC; upper root area, URA; bottom root count, BRC; bottom root area, BRA; root count ratio, RCR; root area ratio, RAR. D) Data generated from RootXplorer is then used to assess the impact of mechanical impedance on root system growth, analyze root penetration dynamics, and evaluate phenotypic variation in root system penetrability across diverse plant species and accessions.
view moreCredit: The authors
The system is designed to mimic mechanical impedance and to identify genotypes with contrasting tolerance to compaction—fast enough to support data-driven breeding decisions.
Deep, penetrative root systems can help crops withstand drought by accessing deeper water and nutrients and may also boost carbon inputs to subsoils. However, breeding for deep rooting is constrained by a measurement gap: soil compaction blocks roots, and “root system penetrability”—the ability of the whole root system to pierce or navigate hard layers—has been difficult to phenotype rapidly, accurately, and at scale. While mechanization has raised farm productivity, it has also intensified anthropogenic subsoil compaction, reducing porosity and increasing mechanical resistance, which restricts rooting depth and contributes to yield losses. Traditional assays are often labor-intensive, invasive, or focused on primary roots, and X-ray CT remains costly and low-throughput.
A study (DOI: 10.1016/j.plaphe.2025.100143) published in Plant Phenomics on 21 November 2025 by Wolfgang Busch’s team, Salk Institute for Biological Studies, establishes RootXplorer as a robust, high-throughput phenotyping platform that enables accurate, whole-root-system assessment of soil penetration, providing a critical foundation for breeding crop varieties resilient to soil compaction, drought, and climate stress.
Using a standardized Phytagel-based cylinder system (CS), the researchers mimicked progressive soil compaction and quantified whole-root-system penetrability across dicots (Arabidopsis, soybean) and monocots (sorghum, rice). They varied Phytagel concentrations in species-appropriate media (MS or Hoagland), scanned plants with the RADICYL multi-view imager, measured bottom-layer mechanical impedance via penetrometer resistance (PR), and manually scored penetration using the root count ratio (RCR) and root area ratio (RAR). To capture secondary stresses associated with compaction, they tested hypoxia using the Arabidopsis pADH::GUS reporter and quantified water availability via media water potential (Ψ). They also validated CS realism with soil columns of different bulk densities (BD), measuring superficial porosity (SP) and PR, and built RootXplorer—an open-source, deep learning (U-Net++) computer-vision pipeline—to automate root segmentation and extraction of RCR/RAR. Results showed PR increased strongly with Phytagel concentration (r = 0.958–0.989), while penetration declined sharply: manual RCR and RAR were strongly negatively correlated with Phytagel concentration (RCR r = −0.954 to −0.991; RAR r = −0.954 to −0.982), dropping from high penetration at low gel (RCR 0.686–0.975; RAR 0.565–3.438) to near-zero at high gel (RCR 0.003–0.046; RAR 0.000–0.101), with Arabidopsis responding even to mild gradients. Low gel (0.4–0.8%) induced hypoxia (high GUS activity), whereas ≥1.0% reduced GUS signal, and increasing gel lowered Ψ (down to −0.518 MPa at 3.0%), indicating rising osmotic stress. Soil compaction similarly reduced SP (r ≈ −0.99) and increased PR, and root penetration fell with BD (r ≈ −0.98), confirming the CS replicates mechanical impedance. RootXplorer achieved high segmentation accuracy (IoU 0.978–0.983) and reproduced negative gel–penetration trends, with automated vs. manual agreement extremely high (RCR r = 0.987–0.997; RAR r = 0.978–0.999), enabling large-scale screening that uncovered substantial natural variation and conserved sorghum tolerance rankings under real soil compaction and X-ray CT.
RootXplorer provides a practical, high-throughput solution for assessing whole-root-system penetrability, enabling hundreds of plants to be screened within hours under standardized mechanical stress conditions. This capability can significantly accelerate the identification of soil compaction–tolerant genotypes for breeding pipelines, advance research into root architectural plasticity and root-type-specific mechanical strategies, and support the selection of deeper-rooting traits associated with improved drought avoidance, enhanced nutrient capture, and potentially increased carbon inputs to subsoils.
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References
DOI
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100143
Funding information
This research was supported by gifts to the Salk Institute's Harnessing Plants Initiative (HPI) from the Bezos Earth Fund, the Hess Corporation, and the TED Audacious Project to W.B., and National Science Foundation (award no. 2243690) and the Governor's University Research Initiative program (05–2018) from the State of Texas grants to L.H.E.
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
Journal
Plant Phenomics
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
RootXplorer: A computer vision-based 3D phenotyping platform for high-throughput quantification and spatio-temporal analysis of root system penetrability
Seeing nutrients in a leaf: How hyperspectral AI reveals grapevine health
Nanjing Agricultural University The Academy of Science
By analyzing how leaves interact with light across hundreds of wavelengths and learning shared patterns among nutrients and pigments, the approach delivers a fast, non-destructive alternative to labor-intensive tissue sampling.
Grapevines require balanced supplies of nutrients such as nitrogen, phosphorus, potassium, calcium, magnesium, and micronutrients to maintain healthy growth. Traditional tissue testing can identify deficiencies early, but it is costly, slow, and provides limited spatial resolution. Remote sensing has long promised a solution, yet many methods rely on simple vegetation indices or single-trait models that struggle with the complex, overlapping spectral signatures of plant traits. Advances in hyperspectral sensing and machine learning now make it possible to move beyond single indicators toward integrative models that reflect how plant traits co-vary and jointly influence leaf reflectance.
A study (DOI: 10.1016/j.plaphe.2025.100142) published in Plant Phenomics on 12 November 2025 by Alireza Pourreza’s team, University of California, points to a scalable pathway for more precise, timely, and spatially detailed nutrient monitoring in vineyards.
The study applied a stepwise modeling framework to improve hyperspectral estimation of grapevine leaf traits by first correcting model bias, then characterizing spectral–trait relationships, completing missing data, and finally comparing predictive strategies. Chlorophyll (Chl) estimates from the PROSPECT-PRO radiative transfer model were first validated against 327 fully labeled samples, revealing systematic overestimation (regression slope 1.33, NRMSE 0.41). A regression-based rescaling (multiplying estimates by 0.75) effectively corrected this bias, producing close agreement with measurements (NRMSE reduced to 0.17) while preserving realistic variability, with mean Chl values of 35.9 μg/cm² in the fully labeled set and 27.2 μg/cm² in the partially labeled set. Spectral importance analysis then identified key wavelength regions for different traits: nitrogen and chlorophyll were most sensitive in the visible range (around 450–550 nm) and near 2200 nm, whereas equivalent water thickness (EWT) and leaf mass per area (LMA) showed stronger responses in the shortwave infrared (around 1200, 1700, and 2200 nm). Correlation and spectral overlap analyses revealed strong inter-trait linkages, such as Mg–Ca (ρ = 0.69) and LMA–Ca (ρ = 0.68), negative relationships between K and both N and Chl, and substantial overlap of informative bands among nutrients and pigments, indicating shared spectral controls. Principal component analysis showed that the first two components explained 94.3% of total spectral variance, confirming strong low-dimensional structure across datasets. To address missing labels, a neural-network imputation model using 23 spectral PCs achieved high accuracy for P, Ca, and Mg (R² ≈ 0.72–0.78), but lower performance for Zn and Mn, reflecting weaker spectral signals. Final trait prediction comparisons demonstrated that a multi-trait CNN–LSTM model consistently outperformed single-trait models across most of 16 traits, with large gains for Mn (R² 0.30→0.62) and leaf structural parameter Nstruct (0.25→0.90) and lower prediction errors overall. Uncertainty analysis showed higher residuals for spectrally dissimilar samples, and filtering low-confidence imputations yielded a robust training set of 925 observations, supporting reliable multi-trait prediction.
The findings demonstrate that multi-trait hyperspectral modeling can deliver accurate, non-destructive assessments of grapevine nutrition at the leaf level. For vineyard managers, this opens the door to earlier detection of nutrient imbalances, more targeted fertilization, reduced input costs, and lower environmental risks. Beyond viticulture, the framework is adaptable to other crops where multiple physiological traits interact to shape spectral signals, supporting broader advances in precision agriculture and crop monitoring.
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References
DOI
Original Source URl
https://doi.org/10.1016/j.plaphe.2025.100142
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
Journal
Plant Phenomics
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
Multi-trait spectral modeling for estimating grapevine leaf traits and nutrients
