# From Chaos to Clarity: Deriving Meaningful Biology from Big Data in Plant Pathology

Nik Cunniffe, Kaitlin M. Gold, Frédéric M. Hamelin, Juan A. Navas-Cortés, Neha Potnis, Alexandra J. Weisberg, Karen A. Garrett

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Nik Cunniffe, Kaitlin M. Gold, Frédéric M. Hamelin, Juan A. Navas-Cortés, Neha Potnis, et al. From Chaos to Clarity: Deriving Meaningful Biology from Big Data in Plant Pathology. Phytopathology, 2025, 115 (10), pp.1240-1244. 10.1094/phyto-10-25-0328-fi. hal-05353943

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# From Chaos to Clarity: Deriving Meaningful Biology from Big Data in Plant Pathology

Nik J. Cunniffe,<fnref n="1" />,† ID Kaitlin M. Gold,<fnref n="2" /> ID Frédéric M. Hamelin,<fnref n="3" /> ID Juan A. Navas-Cortés,<fnref n="4" /> ID Neha Potnis,<fnref n="5" /> ID Alexandra J. Weisberg,<fnref n="6" /> and Karen A. Garrett<fnref n="7" />,<fnref n="8" />,<fnref n="9" /> ID

## Abstract

This Focus Issue was inspired by the vast volumes of data now being generated from both laboratory experiments and field studies. These complex, high dimensional data sets demand new analytic approaches. We invited contributions that apply novel statistical, machine learning, or artificial intelligence methods, or that integrate multiple ‘omics approaches to reveal new aspects of pathogen biology, host interactions, or emergent system-level properties. The Focus Issue contains 13 articles falling into five broad themes and drawing upon a diverse range of data types, from

disease imaging to meta-barcoding data to optical sensing to genetic data to expert knowledge. Together, these papers show how integrating technology, data, and biology is already transforming plant pathology. We hope this Focus Issue inspires new collaborations, new methods, and a continued commitment to turning data into understanding and chaos into clarity.

**Keywords:** disease control and pest management, fungal pathogens, pathogen detection

The persistent and large-scale impacts of plant diseases (Ristaino et al. 2021; Savary et al. 2019) remain key drivers for continued research. Likewise, new technologies that can produce massive amounts of data have emerged, making new types of analyses possible, but requiring new strategies and tools. Addressing these ongoing challenges demands innovation, leading to a potential role for new systems to interrogate complex data sets and uncover insights that traditional methods may overlook. This Focus Issue explores how data-intensive and computational approaches are shaping the future of plant pathology.

There is a clear need for such methods. Plant pathologists can now generate vast volumes of data from laboratory experiments and field studies. Environmental variables are recorded at fine spatial and temporal resolutions (Rajak et al. 2023), while remote sensing technologies capture information at scales ranging from single plants to countries and continents (Oerke 2020). At the molecular level, researchers can use a wide range of ‘omics technologies, including genomics, transcriptomics, metabolomics and phenomics, to interrogate host-pathogen interactions at unprecedented resolution (Jain et al. 2024). Powerful analytical tools, including new methods in epidemiological modeling (Cunniffe and Gilligan 2020; Madden 2025) and increasingly accessible machine learning frameworks, make it possible to extract meaning from complex,

high-dimensional data sets. These methods are transforming how we detect, predict, understand, and manage epidemics. Ultimately, effective use of new big data sources will be key to implementing global surveillance and mitigation systems that respond effectively to changing disease management scenarios (Carvajal-Yepes et al. 2019; Garrett et al. 2022; Prasanna et al. 2022).

This Focus Issue was inspired by these developments. As always for Phytopathology, our call for papers solicited articles that would significantly advance our understanding of plant diseases. We asked for contributions that apply innovative statistical, machine learning, or artificial intelligence methods, or that integrate multiple ‘omics approaches to reveal new pathogen biology, host interactions, or emergent system-level properties. Our goal was to showcase how computational, mathematical, and statistical approaches can drive meaningful biological insights from big data.

Our Focus Issue brings together 13 papers, falling into five interrelated broad themes.

1. Innovations in data generation and analysis. Three papers highlight recent advances and the need for analytic rigor in large-scale data workflows, ranging from high-throughput phenotyping (Brewer et al. 2025; Sapkota et al. 2025) to metabarcoding pipelines (Sudermann et al. 2025).

2. Predictive modeling and risk forecasting. Three papers (Chauvin et al. 2025; Mao et al. 2025; Shin et al. 2025) show how advances in machine learning and model ensemble methods can improve forecasts of disease risk.

3. Novel uses of machine learning. Two papers demonstrate new potential uses of machine learning, by identifying interpretable predictors in statistical models (Alves et al. 2025), or for interpreting remote sensing data in union with epidemiological modeling (Mikaberidze et al. 2025).

<figure><img src="image_1.png" /></figure>

The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2025.

<footnote n="1">¹ Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, U.K.</footnote>

<footnote n="2">² Plant Pathology & Plant Microbe Biology Section, Cornell University, Cornell AgriTech, 15 Castle Creek Drive, Geneva, NY 14456, U.S.A.</footnote>

<footnote n="3">³ Institut Agro, Univ Rennes, INRAE, IGEPP, 35000, Rennes, France</footnote>

<footnote n="4">⁴ Institute for Sustainable Agriculture, CSIC, Avda. Menéndez Pidal s/n. 14004 Córdoba, Spain</footnote>

<footnote n="5">⁵ Entomology and Plant Pathology, Auburn University, 209 Rouse Life Science Building, Auburn, AL 36849-5412, U.S.A.</footnote>

<footnote n="6">⁶ Botany and Plant Pathology, Oregon State University, OSU Botany 2701 SW Campus Way, Corvallis, OR 97331, U.S.A.</footnote>

<footnote n="7">⁷ Plant Pathology Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, U.S.A.</footnote>

<footnote n="8">⁸ Global Food Systems Institute, University of Florida, Gainesville, FL 32611, U.S.A.</footnote>

<footnote n="9">⁹ Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, U.S.A.</footnote>

<footnote>Accepted for publication 9 October 2025.</footnote>

<footnote>†Corresponding author: N. J. Cunniffe; njc1001@cam.ac.uk</footnote>

<footnote>The author(s) declare no conflict of interest.</footnote>
4. *Non-digital big data: The role of expert knowledge.* One article (Robledo et al. 2025) reminds us that big data need not necessarily be digital, but instead can reside in the minds of subject specialists, highlighting the potential role of expert elicitation.

5. *Pathogen biology from molecular insights.* Four papers (Cauldron et al. 2025; Chan et al. 2025; Higuita et al. 2025; Kaur et al. 2025) use big data to provide new insights into pathogen diversity, invasion, evolution, and molecular mechanisms.

The papers illustrate a wide range of pathways to integrate small and big data in new analyses for greater understanding of plant pathology (Fig. 1). Together, these papers illustrate how integrating technology, data, and biology is already beginning to transform Chaos to Clarity, shaping the future of plant pathology.

## Innovations in Data Generation and Analysis

Two papers in the Focus Issue highlight innovations in high-throughput phenotyping to enhance the scale and power of experiments characterizing and mapping quantitative trait loci (QTLs). This is important, since manual phenotyping is laborious, time consuming, and requires trained experts to correctly classify samples. Sapkota et al. (2025) developed a nondestructive, automated microscopy platform combined with a convolutional neural network to analyze foliar symptoms of powdery mildew on grapevine. Their platform was not only dramatically faster than manual microscopy, but also more accurate at capturing phenotypic variation. Importantly, the authors highlight the power of their approach by identifying a previously unknown resistance locus, *REN13*, which could be used in breeding mildew-resistant cultivars. This

imaging platform and neural network analysis strategy greatly expands the scale, power, and speed of QTL mapping.

However, while automated imaging and phenotyping approaches are potentially powerful, they are not immune to error. Brewer et al. (2025) investigated the robustness of QTL mapping by introducing various forms of data degradation, such as swapping samples, changing the scale of measurements, or removing replicates, into imaging data sets. Encouragingly, QTL mapping from high-throughput phenotyping data was robust to most types of error. Small amounts of sample swapping or errors in measurement for single replicates had little effect on the ability to identify known QTLs at all effect sizes. Lack of sample replicates had the greatest effect on the ability to detect known QTLs; however, the effects of these errors are modest. These results suggest the advantages of high-throughput imaging will not be compromised by common sources of error.

Another area requiring new tools is the scalable analysis of metabarcoding data (Licata et al. 2025). Sudermann et al. (2025) present a new bioinformatic tool, demulticoder, to streamline the analysis of large-scale metabarcoding data, and which focuses on diverse and multiplexed data, common in large ecological and microbiome studies (Callahan et al. 2016). The pipeline also allows for the simultaneous analysis of multiple barcode regions (ITS1, rps10) and integration of data from separate experiments. This flexibility is essential for scaling studies that require integration of diverse data types, such as combining bacterial, fungal, and oomycete communities in one analysis. The package has a modular design, enabling researchers to perform iterative quality control, meaning that they can refine specific parts of the pipeline without having to re-run the entire workflow, crucial for dealing with noisy or heterogeneous data (Martin and Rybicki 2000).

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Articles in this Focus Issue address a wide range of data types and present new approaches for analyses.</figcaption></figure>
Predictive Modeling and Risk Forecasting

Three papers in the Focus Issue show how modern develop-
ments in machine learning can improve predictions of disease
risk. Chauvin et al. (2025) applied ensemble modeling to identify
risk factors and predict disease severity for virus yellows in sugar
beet, a major viral disease threatening European sugar production.
Drawing on more than 150,000 statistical and machine learning
models trained on extensive epidemiological, climatic, and land-
use data, they show that aggregating across models yields more
robust and interpretable predictions than relying on a single best
model. Their analysis highlights the role of winter and spring tem-
peratures, spring humidity and precipitation, surrounding land-use
(notably cereal crops and grasslands), and proximity to seed pro-
duction fields as major drivers of virus yellows risk. By combining
diverse data streams with ensemble approaches, this study illustrates
how big data can be translated into meaningful epidemiological
insights.

Using Fusarium head blight in wheat as a case study, Mao et al. (2025) tackle a classic bottleneck in plant disease forecasting: how to turn long, messy weather histories into early, actionable risk signals. Using 55 years of daily climatology (temperature, relative humidity, precipitation, and sunshine hours), they extracted a large library of time-series descriptors (including Fourier/phase, entropy, matrix-profile, and other complexity features), then refine these to nine informative pre-anthesis features that generalize across regions. Six models were compared (artificial neural network [ANN], support vector machines [SVM], logistic regression, K-nearest neighbors [KNN], random forest, and XGBoost). The authors find that the optimized ANN leads with ~91% accuracy/F1 in classifying epidemic levels (low/medium/high). They go on to use SHAP (SHapley Additive exPlanations) analyses, a method of interpreting how important different features are in driving the output of a machine learning model (Lundberg et al. 2020) that has hitherto been little used in plant pathology, albeit with some exceptions (Taylor and Cunniffe 2023), to expose biologically sensible nonlinear effects (e.g., seasonal phase and variability of temperature/humidity). Notably, adding a debris-based inoculum index did not improve performance over climatology alone, underscoring that well-chosen temporal signatures in weather can capture much of the epidemic signal at scale. The result is a compact, interpretable, and portable forecasting recipe: a small set of robust climate features combined with a lightweight classifier that can be deployed before infection pressure peaks. This paper exemplifies the kind of reproducible, data-efficient pipeline needed to move from surveillance to timely management.

In a Short Communication, Shin et al. (2025) explore how sea-
sonal weather dynamics influence disease epidemics, using two im-
portant diseases—panicle blast and grain rot in rice—as a case study.
They trained long short-term memory recurrent neural networks
on meteorological data to predict disease occurrence. Through
gradient-based analysis, they examined how specific weather pat-
terns relate to disease development. A key strength of their approach
is its minimal data requirements—relying solely on meteorological
inputs. While model accuracies indicated that further refinement
is needed for real-world application, the study provides a valuable
proof of concept. It demonstrates how weather-based time-series
data can support scalable, data-driven disease forecasting, particu-
larly if this could be combined in future with biological knowledge
or additional data sources.

Novel Uses of Machine Learning

The papers above highlight how machine learning can improve
disease risk prediction. However, since field datasets on plant dis-
ease epidemics often contain relatively few observations or lack
replication, the “big data” required for machine learning approaches
might not be available. This poses a challenge in applying machine

learning to plant disease epidemiology. Two papers in the Focus
Issue concentrate on this idea.

Alves et al. (2025) highlight an important potential use of ma-
chine learning: not in building new models, but instead in guiding
the development of simpler, less data-intensive but more robust
models. They show how machine learning might be used to cou-
ple typical “small data” on disease spread to the vast array of
environmental data that are now routinely collected and readily
available, by identifying which predictors would be most useful
in classical regression approaches. The idea is demonstrated using
a data set on white mold in 365 commercial snap bean fields in
central and western New York State, coupling their georeferenced
disease observations to a diverse range of soil-type and environ-
mental data. Random forest models trained on this data set showed
impressive performance, but suffered from over-fitting, and so with-
out further analysis have little predictive power and reveal few
insights. By using SHAP analysis to identify the most informa-
tive predictor co-variates in their random forest models, a simpler,
four-predictor logistic regression model could be developed, which
showed impressively good predictive performance.

Optical sensing also generates vast volumes of data, a challenging and exciting frontier for integration with epidemiological modeling. A team representing a large cross-section of scientists working at this frontier developed a vision for research and implementation (Mikaberidze et al. 2025). They systematically identified key features that can be estimated using optical sensing, how this data can inform epidemiological modeling, and in turn how epidemiological modeling can support improved optical sensing. For example, optical sensing can be used to identify inoculum reservoirs or alternative hosts, and epidemiological modeling can be used to target optical sensing to locations most important for analysis. Challenges for integration of optical sensing and epidemiological modeling, identified by the team, include identification of individual diseases from optical sensing data, data quality and availability, including limited sources of ground truth data, and compatibility between existing data and data needs. Mikaberidze et al. (2025) also drew on their broad experience to develop a set of recommendations for integration. These recommendations range from the development of data standards and optimization strategies to interdisciplinary collaboration and capacity development to make the most of new opportunities for integration. These recommendations can help guide plant pathologists and their collaborators to make the best use of this dimension of big data.

Nondigital Big Data: The Role of Expert Knowledge

Robledo et al. (2025) describe how expert knowledge elicita-
tion can be harnessed as a unique form of “big data” in plant
pathology. While genomic, sensor, and environmental data streams
are increasingly digitized, much of the most valuable information
about plant disease dynamics still resides in the minds of experts,
based on decades of field experience, extension work, and unpub-
lished observations. This article highlights structured elicitation
methods that can transform such tacit insights into reproducible,
scalable datasets—ranging from survey responses to digital audio
and video—capable of integration with Bayesian inference and ar-
tificial intelligence pipelines. The authors draw on lessons from
global case studies to illustrate how expert-derived information can
fill critical knowledge gaps when objective data are sparse, costly,
or unattainable, for example in the context of emerging diseases
or informal seed trade. By framing human expertise as a dynamic,
rapidly updated “walking database,” Robledo et al. (2025) argue
that expert elicitation offers a rigorous and cost-effective comple-
ment to conventional big data streams, while also pointing to future
opportunities for coupling these approaches with machine learning
and natural language processing to advance plant health decision-
making. This is an important perspective on these techniques which
have already found application in policy-focused contexts, most
often for invading or potentially invading pathogens when other
sources of data can often be unavailable (European Food Safety
Authority 2014).

Pathogen Biology from Molecular Insights

Four papers illustrate how big data and modern analyses pro-
vide new insights into pathogen diversity, evolution, and molecular
mechanisms. While horizontal gene transfer (HGT) is a major driver
of bacterial evolution, gene flow among plant pathogens is often
constrained by ecological, genetic, and physiological factors
(Weisberg and Chang 2023; Weisberg et al. 2020). Kaur et al. (2025)
explore the diversity and distribution of plasmids in Xanthomonas
euvesicatoria pv. perforans, a major pathogen responsible for bacte-
rial spot of tomato and pepper. Using k-mer-based network analysis,
the authors identified plasmid cliques confined to specific lineages,
reflecting limited HGT and suggesting that plasmid-mediated gene
flow is more restricted. These findings challenge the assumption
of unrestricted plasmid flow and underscore the importance of
ecological and evolutionary constraints in HGT models. The au-
thors leverage the power of large-scale isolate genome sequencing,
genome-wide k-mer analysis, network analysis to provide high-
resolution insights into plasmid diversity and mobility. This aligns
with broader trends in microbial genomics, where multi-omics inte-
gration, machine learning and pangenome analysis are transforming
our understanding of pathogen evolution and disease dynamics
(Acman et al. 2020; Weisberg et al. 2020).

Plants and pathogens are engaged in an ongoing evolutionary
arms race. Type III secreted effectors (T3SEs) are a major weapon
in the bacterial plant pathogen arsenal. However, these secreted pro-
teins can be a double-edged sword, functioning to increase virulence
on a host, but eliciting strong host defenses if detected by the plant.
The known T3SEs are genetically diverse, and it is unclear how
these gene families relate across diverse bacterial pathogen genera.
Chan et al. (2025) characterized the diversity and relatedness of
T3SE genes across all sequenced strains of six major phytopathogen
genera. They clustered the sequences of thousands of effectors
into related groups and developed a proposed nomenclature sys-
tem for describing T3SE families across genera. They found that
while some T3SE families are constrained to specific bacterial taxa,
others are present across multiple genera. This dataset and nomen-
clature system enables researchers studying different pathogens to
communicate effectively and better predict T3SE function.

Cauldron et al. (2025) demonstrate how large-scale genomic
data can bring clarity to the dynamics of clonal invasions in forest
pathology. By sequencing and analyzing more than 290 genomes
of *Phytophthora ramorum* collected during the early phases of two
independent introductions (NA1 and EU1 lineages) into Oregon
forests, the authors transform complex population-level variation
into biological insight about invasion processes. Their results reveal
how clonal populations generate diversity not only through muta-
tion but also via loss of heterozygosity, allowing them to overcome
genetic bottlenecks. Importantly, the contrasting trajectories of the
NA1 and EU1 epidemics underscore how comparative genomic
“big data” can disentangle the hidden mechanisms that enable inva-
sive pathogens to establish and persist in natural ecosystems. This
study exemplifies the power of population genomics to convert ap-
parent chaos in invasion dynamics into meaningful understanding
of pathogen evolution and ecology.

Higuita et al. (2025) used natural language processing and se-
quence data mining to characterize the global diversity of viruses
associated with the *Passifloraceae*, a plant family comprising many
economically important tropical fruits. Higuita et al. (2025) used
natural language models to mine the published literature and iden-
tify associations of viruses with *Passifloraceae* hosts across multiple
decades. The authors also analyzed all publicly available *Passiflora*
transcriptome data for viral sequences to characterize the presence

and diversity of different viral families associated with this host.
Finally, they used network analysis to overlay these datasets and
find correlations between each approach. This novel approach iden-
tified dozens of viral species associated with the Passifloraceae,
including previously unknown associations. The authors identified
multiple incidences of mixed viral infections, which may compli-
cate strategies for mitigation. They also found associations between
viral species and specific geographic locations or plant host species.

Conclusion

Taken together, the papers in this Focus Issue highlight how the
application of computational tools, grounded in biological knowl-
edge, can extract clarity from complexity. Several papers emphasize
the importance of interpretability, expert knowledge, and data ro-
bustness, reminding us that even the largest data sets and most
advanced analytic tools are no panacea. Fusing big data analysis
with domain expertise will remain essential, meaning we all have a
role to play. In this spirit, we hope this Focus Issue inspires new col-
laborations, new methods, and a continued commitment to turning
data into understanding and chaos into clarity.

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