# Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modelling

Alexey Mikaberidze, C. D. Cruz, Ayalsew Zerihun, Abel Barreto, Pieter Beck, R Calderon, Carlos Camino, Rebecca E. Campbell, Stephanie K. L. Delalieux, Frédéric Fabre, et al.

► To cite this version:

Alexey Mikaberidze, C. D. Cruz, Ayalsew Zerihun, Abel Barreto, Pieter Beck, et al. Opportunities and Challenges in Combining Optical Sensing and Epidemiological Modelling. Phytopathology, 2025, 115 (10), pp.1260-1285. 10.1094/phyto-11-24-0359-fi. hal-05100639

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# Opportunities and challenges in combining optical sensing and epidemiological modelling

Alexey Mikaberidze¹, C. D. Cruz²,*, Ayalsew Zerihun³,*, Abel Barreto⁴, Pieter Beck⁵, Rocío Calderón⁶, Carlos Camino⁵, Rebecca E. Campbell⁷, Stephanie K.L. Delalieux⁸, Frédéric Fabre⁹, Elin Falla¹⁰, Stuart Fraser¹¹, Kaitlin M. Gold⁶, Carlos Gongora-Canul², Frédéric Hamelin¹², Dalphy O.C. Harteveld¹³, Cheng-Fang Hong¹⁴, Melen Leclerc¹², Da-Young Lee¹⁵, Murillo Lobo Jr.¹⁶, Anne-Katrin Mahlein⁴, Emily McLay¹¹, Paul Melloy¹⁷, Stephen Parnell¹⁸, Uwe Rascher¹⁹, Jack Rich¹,²⁰, Irene Salotti²¹, Samuel Soubeyrand²², Susan Sprague²³, Antony Surano²⁴, Sandhya D. Takooree²⁵, Thomas H. Taylor²⁶, Suzanne Touzeau²⁷, Pablo J. Zarco-Tejada²⁸,²⁹, Nik J. Cunniffe¹⁰,²⁰,*

¹School of Agriculture, Policy and Development; University of Reading; Reading, United Kingdom. ²Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, United States. ³Centre for Crop and Disease Management, School of Molecular and Life Sciences, Curtin University, Perth, WA6845, Australia.

⁴Institute of Sugar Beet Research, Göttingen 37079, Germany. ⁵European Commission (EC), Joint Research Centre (JRC), 21027 Ispra, Italy. ⁶Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Sciences, Cornell AgriTech, Cornell University, Geneva, NY, United States. ⁷Epidemiology & Disease Management, The New Zealand Institute for Plant and Food Research Ltd, Motueka 7198, New Zealand.

⁸Remote Sensing Department, Flemish Institute for Technological Research, VITO, Mol, Belgium. ⁹INRAE, Bordeaux Sciences Agro, SAVE, 33882 Villenave d'Ornon, France. ¹⁰Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom. ¹¹Scion, Titokorangi Drive, Private Bag 3020, Rotorua 3046, New Zealand. ¹²GEPP, INRAE, Institut Agro, University of Rennes, Rennes, France. ¹³Division of Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research, Ås, Norway. ¹⁴Department of Plant Pathology, National Chung Hsing University, Taichung, Taiwan. ¹⁵Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea. ¹⁶Embrapa Rice and Beans, Santo Antônio de Goiás, Brazil. ¹⁷School of Agriculture and Food Sustainability, The University of Queensland, Australia. ¹⁸The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Coventry, United Kingdom. ¹⁹Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungsznetrum Jülich GmbH, Leo-Brandt-Str, 52425 Jülich, Germany. ²⁰Girton College, University of Cambridge, Cambridge, United Kingdom. ²¹Department of Sustainable Crop Production (DI.PRO.VES.), Università Cattolica del Sacro Cuore, Piacenza, Italy. ²²INRAE, BioSP, 84914, Avignon, France.

²³CSIRO Agriculture and Food, GPO Box 1700, Canberra ACT 2601, Australia. ²⁴Institute for Sustainable Plant Protection, National Research Council, Bari, Italy. ²⁵Department of Agricultural and Food Science, Faculty of Agriculture, University of Mauritius, Reduit, Mauritius. ²⁶The Pirbright Institute, Pirbright, United Kingdom.

²⁷Université Côte d'Azur, INRAE, ISA, France & Université Côte d'Azur, Inria, INRAE, CNRS, MACBES, France.

²⁸Institute for Sustainable Agriculture (IAS), Spanish Council for Scientific Research (CSIC), Cordoba, 14004, Spain. ²⁹School of Agriculture, Food and Ecosystem Sciences (SAFES) - Faculty of Science, and Faculty of Engineering and Information Technology (FEIT), University of Melbourne, Melbourne, Victoria, 3010, Australia.

## Author Contributions

All authors contributed to drafting text for one or more subsections of the paper and to editing the final manuscript. Some co-authors had additional responsibilities: organised the ICPP satellite meeting - AM, PB, CC, FH, FF, SP, ST, NJC; led writing individual subsections or components of the paper - AKM, AM, AB, CDC, RC, AF, FF, KMG, DYL, ML, PM, SP, UR, ST, PJZ-T, NJC; led writing the initial draft of the final paper - AM, NJC (with input from CDC and AZ).

<footnote>* Corresponding authors</footnote>
## Abstract

Plant diseases impair yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, since traditional methods to measure plant diseases are resource intensive and this often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral and thermal imaging, and light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, aeroplanes and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modelling have rarely been integrated. To address this gap, we first review the state-of-the-art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modelling. We discuss how optical sensing can inform epidemiological modelling by improving model selection and parameterisation and providing accurate maps of host plants. Epidemiological modelling can inform optical sensing by boosting measurement accuracy, improving data interpretation and optimising sensor deployment. We consider outstanding challenges in: A) identifying particular diseases; B) data availability, quality and resolution; C) linking optical sensing and epidemiological modelling; and D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose to standardise methods and protocols for optical sensing of plant health and develop open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work.

## Introduction

Plant diseases affect yield, quality and profitability of crops and forestry products. Estimated impacts vary, making it difficult to unambiguously quantify losses (Oerke, 2006; Savary et al, 2019; Savary et al, 2023). However, consequences of disease can be substantial and can even impact food security (Strange and Scott, 2005). Analogous impacts upon ecosystem services are caused by pathogens of natural vegetation (Boyd et al, 2013). Some pathogens are endemic, routinely causing disease in locations within which they are well-established, at least in the absence of management. Other pathogens are emerging, i.e., increasing in incidence, or geographic range, or host range (Ristaino et al, 2021). Outbreaks of emerging pathogens are increasingly well documented (Rosace et al, 2023; Jeger et al, 2023; Fielder et al, 2024), and rates of invasion are escalating (Ristaino et al, 2021).

Plant disease epidemics develop across multiple spatial and temporal scales. Models tracking the dynamics of disease in time and space, and the epidemiological mechanisms causing these dynamics, have been improved and have become increasingly popular over the past few decades (Madden et al., 2007; Gilligan 2008). The current state-of-the-art (see below) often involves complex spatiotemporal epidemic models fitted using advanced Bayesian techniques (e.g., Soubeyrand et al., 2009; Pleydell et al., 2018; Godding et al., 2023). Modelling provides a rational basis to integrate what is known with what is unknown, but can reasonably be inferred, to predict the future epidemic dynamics. Predictions from such models can then be used to design surveillance and control strategies (Parnell et al., 2017; Cunniffe and Gilligan, 2020). However, to make concrete predictions for a specific pathosystem, models must be fitted to and validated using experimental or observational data, and lack of suitable data is often a significant limiting factor.

In part, this data limitation is because traditional methods for the detection and quantification of plant diseases are time and resource intensive, largely since they involve human observers (Bock et al. 2020). Proximal and remote sensing - which can be distinguished
from each other in terms of distances separating sensor and target (Oerke, 2020) - have great potential in this context. Many pathogens cause changes in plant health that can be detected not only in the visible spectral range but also beyond that range (Mahlein et al., 2024). Among many examples are tan spot on wheat leaves (caused by the fungus *Drechslera tritici-repentis*) that results in a characteristic reduction in reflectance in the near-infrared plateau (Bohnenkamp et al., 2021) and latent infections by *Venturia inaequalis* (apple scab) that were detected as spots of lower temperature by capturing light in the thermal infrared range (Oerke et al., 2011). Use of optical sensing to measure these signals is thus particularly attractive. Here we use "optical sensing" as a common term to describe a range of proximal and remote sensing techniques making use of electro-magnetic radiation across a potentially wide spectral domain, including ultraviolet (UV; a list of acronyms used is given in Table 1), visible and infrared (IR).

Optical sensing technologies and platforms have advanced in the past decades, meaning that cheap uncrewed aerial vehicles (UAVs), standard piloted aircraft carrying affordable imaging sensors, and spaceborne systems collecting ever higher-resolution (spatial and spectral) imagery have become available (Jin et al., 2021a). As a result, a portfolio of digital systems can now deliver optical sensing data at unprecedented spatial, spectral and temporal resolutions and scales. Optical sensing of vegetation is now a leading focus in remote sensing science, allowing us to use nested data that span a wide range of spatial scales (Gamon et al., 2019). Further developments, including hyperspectral satellite imagery at high temporal and spatial resolutions, will accelerate use of remote sensing data to detect and map disease and inform epidemiological modelling.

In plant disease research, there is a significant focus on epidemiology and modelling. However, it is hitherto uncommon for modellers to use optical sensing derived measurements of plant diseases. Although there are some exceptions in which optical sensing is used to inform summaries such as logistic or Gompertz disease progress curves (e.g., Gongora-Canul et al., 2020; Zhang et al., 2023), only few papers make meaningful links between optical sensing and the state-of-the-art approaches in epidemiological modelling (e.g., Camino et al., 2021, Leclerc et al., 2023). Indeed, in part due to deficiencies in current training programmes and a lack of training focusing on applied data science, most individuals interested in sensing technologies for plant disease do not have a background in epidemiological modelling. On the other hand, disease modellers, who can often be skilled data scientists, generally lack understanding of the opportunities and challenges involved in processing and interpreting remotely sensed information. Significant links between the optical sensing community and disease modellers remain absent, despite the logical benefits of such collaboration (Heim et al., 2019).

Excited by the possibilities of building such links, a subset of the authors of this paper organised a Satellite Meeting of the 2023 International Congress of Plant Pathology in Lyon: "How to combine remote sensing with epidemiological modelling to improve plant disease management?". By assigning all attendees preparatory work focusing on identifying challenges in linking the fields, and by making time for didactic talks in the meeting's programme (archive: https://reseau-modstatsap.mathnum.inrae.fr/episense), attendees from backgrounds predominantly in remote sensing or epidemiological modelling were able to engage and to discuss. By working collaboratively, we came to a consensus view on the opportunities - and challenges - in linking the two fields and allowing epidemiological modelling to inform work in optical sensing, and vice versa.

This paper is the output of this work. We review the opportunities and challenges in combining optical sensing with epidemiological modelling. We start by describing the state-of-the-art in each field. Although thorough reviews of both fields are available (e.g., Oerke, 2020; Bock et al. 2020; Gilligan and van den Bosch, 2008; Cunniffe and Gilligan, 2020; Fabre et al., 2021; Mahlein et al., 2024), in part we wanted to use this paper to develop a
common language accessible to both research communities. This requires a more detailed explanation. To illustrate what might be possible, we highlight opportunities for optical sensing to contribute to epidemiological modelling, and *vice versa*. We then review the outstanding challenges and categorise them into those associated with: A) identifying particular diseases; B) data availability, quality and resolution; C) linking optical sensing and epidemiological models; D) emerging diseases. We conclude with a set of recommendations, to provide a road map to motivate and shape future research and practice in both fields.

# Current state of the art

## Optical sensing of plant diseases

### Sensors

The most commonly used sensors are standard red-green-blue (RGB) and colour-infrared (CIR) cameras. These are affordable and portable, and capable of millimeter-scale spatial resolution when used in proximal sensing settings (Barbedo 2016; Bock et al., 2020; Anderegg et al., 2024). However, such sensors only capture images in three spectral bands, reducing the number of spectral characteristics that can be monitored. Despite their fine spatial resolution and low prices, RGB and CIR cameras are optimised to reflect human vision and thus do not provide quantitative measurements of light reflection and absorption.

Multispectral imaging systems, in contrast, operate across multiple discrete spectral bands, and are often designed to quantitatively measure the intensity of electromagnetic radiation. Since the spectral bands tend to be narrower than those used in RGB and CIR sensors, this enables more precise estimation of changes in specific absorption features. They also often cover spectral regions beyond the visible, enabling characterisation of pigments or structural plant traits (Xie et al., 2008; Blasch et al., 2023). Hyperspectral imaging (aka HSI or imaging spectroscopy) captures light across a much wider spectral range in narrow contiguous bands, including ultra-violet (UV; 250-400 nm wavelength), visible (400-700 nm), near-infrared (NIR; 700-1300 nm) and shortwave infrared (SWIR; 1300-2500 nm), and has a high spectral resolution (Fiorani et al. 2012; Mishra et al., 2017; Mahlein et al., 2019; Sarić et al., 2022; Rayhana et al., 2023; Brugger et al., 2023).

Very generally, plant and fungal pigments (e.g., chlorophyll, anthocyanins, carotenoids, melanins) affect reflectance spectra in the UV and visible ranges (Gay et al., 2008; Bohnenkamp et al., 2019b; Brugger et al., 2023). Reflectance in the visible and NIR/SWIR ranges carries information about foliar plant traits relevant to disease, including nutrient and water content, photosynthetic capacity, pigment and phenolic compound concentration, as well as other physiological and morphological properties of plants including leaf area index (Delalieux et al., 2008; Singh et al., 2015; Mishra et al., 2017; Mahlein et al., 2019; Vanbrabant et al., 2019; Gold et al. 2019a,b; Garrett et al., 2022). Reflectance in the red edge area (680-750 nm) is sensitive to plant stress, because it is affected by chlorophyll absorption (Horler et al., 1982). HSI has been extended to retrieve passive solar-induced fluorescence (SIF) in the field and with airborne hyperspectral sensors (Mohammed et al., 2019), in contrast to classical chlorophyll fluorescence that is mainly limited to controlled environments. This makes HSI more useful for disease measurement (Calderón et al. 2013; Mahlein et. al. 2018; Zarco-Tejada et al. 2018) and monitoring (Porcar-Castell et al. 2021). HSI can quantify subtle changes in plant constituents and the rich information content of hyperspectral data is promising for disease detection and quantification.

Recent publications have established scalable detection of multiple economically important diseases caused by bacterial (Zarco-Tejada et al., 2018; Schoofs et al., 2020), fungal
(Sapes et al., 2022), oomycete (Hornero et al., 2021), and viral (Romero Galvan et al., 2023) pathogens asymptomatically with visible-SWIR hyperspectral imagery collected via aircraft. Once the most discriminatory wavelengths are identified, hyperspectral sensors may be replaced with cheaper multispectral sensors which capture fewer spectral bands located at the most informative spectral regions sensitive to the biotic-induced physiological changes (Bohnenkamp et al., 2019b; Poblete et al., 2020).

Thermal infrared (TIR) imaging (aka thermography) captures radiation in the long-infrared, thermal range (8-14 µm wavelength), providing information complementary to HSI. Typical outputs include maps of canopy or leaf temperature normalised by air temperature (Still et al., 2019), thermal-based indices such as the crop water stress index (CWSI; Jackson et al., 1981) and the index of stomatal conductance (Jones 1999). For foliar diseases, plant-pathogen interactions can disrupt stomatal function, leading to changes in temperature within affected leaf areas (e.g. Bassanezi et al., 2002; Hellebrand et al., 2006; Smith et al., 1986). Vascular pathogens can block plant vessels, which reduces transpiration rates, and this can also be quantified by TIR imaging (e.g., Calderon et al., 2015; Zarco-Tejada et al., 2018). TIR imaging in controlled environments has achieved pre-symptomatic detection in several pathosystems (e.g., Chaerle et al., 2004; Oerke et al., 2011), although reliable signals of pre-symptomatic disease appear absent for others (Pineda et al., 2021). In the field, higher severities of Dothistroma needle blight in pine trees and septoria tritici blotch in wheat have been associated with increased canopy temperatures via TIR imaging (Smigaj et al., 2019; Wang et al., 2019). TIR imaging is potentially a powerful tool for detecting plant stress (Messina and Modica, 2020). However, its outputs are not pathosystem specific and can be confounded with abiotic stress (Pineda et al., 2021; Kuska et al., 2022), and even without stress, temperature distributions in field canopies vary in space and time. Hence, TIR imaging is expected to be most useful in combination with other sensing technologies (Berger et al., 2022).

Light detection and ranging (LiDAR) is an optical sensing technology that uses reflected laser pulses to measure distances (Wang and Menenti, 2021), generating dense 3D point clouds to map an environment. The technology is increasingly used to measure structural characteristics of plants (Omasa et al., 2007), especially crops (Jin et al., 2021b; Rivera et al., 2023). Applications include detecting individual plants, classifying them according to species (Fassnacht et al., 2016), and estimating plant height, leaf area index (Wang and Fang, 2020), canopy density and volume, dry matter and yield. Since structural and geometric plant traits captured by LiDAR can be affected by pathogens, in principle LiDAR can also be used to measure plant diseases, although examples are rare (see, for example, Husin et al., 2020). More often LiDAR has been used in conjunction with other sensing techniques, e.g., for Dothistroma needle blight (Smigaj et al., 2019) or wilt disease (Yu et al., 2021), and for vascular wilt ('Blackleg') disease in potato (Franceschini et al., 2024), since LiDAR provides information complementary to other sensing methods.

## Platforms and spatiotemporal scales

Several platforms have been developed to gather proximal and remote sensing measurements (Jin et al., 2021a). Some platforms are stationary, fixed in place by poles (Parmentier et al., 2021), cable suspension (Kirchgessner et al., 2017), or rails (Virlet et al., 2017). Others are mobile, ranging from hand-held (Cerovic et al., 2012; Behmann et al., 2018), to those mounted on human-driven (Buelvas et al., 2023) and/or robotic vehicles (Underwood et al., 2017; Cubero et al., 2020; Pearson et al., 2022), to uncrewed aerial vehicles (UAVs) (Sankaran et al., 2015; Aasen et al., 2018; Kim et al., 2019; Kouadio et al., 2023), to piloted aircrafts (Kampe et al., 2010; Wang et al., 2020), to high altitude balloons (Hobbs et al., 2023), and satellites (Rast and Painter, 2019; Paek et al., 2020; Qian, 2021).
The features of the sensor-platform combination determine the spectral, temporal and spatial characteristics of the observations and typically trade off detail (resolution), scale (extent), and fidelity (precision and accuracy) (we discuss these tradeoffs in more detail in Challenge Biii). We note that, because the sensors and platforms are undergoing rapid development, these trade-offs are continuously changing. Mass production of UAV components makes it now possible to relatively cheaply and regularly collect spatially detailed plot or landscape-scale images that until recently required piloted aircrafts. Thanks to the miniaturisation of sensors, both piloted aircrafts and UAVs can carry sensors chosen for their sensitivity to specific vegetation traits of interest to an epidemiological problem (Jin et al., 2021a).

New and forthcoming imaging spectroscopy satellites include the German Aerospace Center's Environmental Mapping and Analysis Program (EnMAP) (Storch et al. 2023; Chabrillat et al., 2024), NASA's Surface Biology and Geology (SBG) (Cawse-Nicholson et al. 2021), Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) (Tagliabue et al., 2022) and ESA's Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) (Celesti et al., 2022). These will provide vast open datasets which can be used for plant disease measurement, with smaller missions like CSIMBA-IPERLITE (a non-commercial in-orbit demonstration mission of the EU) adding hyperspectral capacity at higher spatial resolution (≈20 m) (Livens et al., 2024). These systems provide high spectral and temporal resolutions (sub-monthly), but intermediate spatial resolutions (≈30 m). Current thermal imaging satellites, such as NASA's ECOSTRESS, have insufficient spatial resolution for effective plant disease monitoring (>100 m). However, upcoming high-resolution TIR satellite sensors, such as NASA's Landsat Next and ESA's Land Surface Temperature Monitoring (LSTM), will offer improved revisit intervals (3-6 days) and spatial resolutions (50-60 m).

These advances promise to improve characterisation of plant diseases, but the relatively coarse spatial resolution remains a challenge. The commercial satellite industry has sought to fill this gap. Recent developments in satellite design have improved the spatial-temporal resolution and scalability of spaceborne sensing platforms, making them more suitable for disease detection (Kanaley et al., 2024; Poblete et al., 2023; Raza et al., 2020). Largely, this has become possible thanks to developing satellite constellations, groups of satellites working together, often designed to complement each other in terms of coverage, revisit time, or other functions. For example, Planet Lab's cube multispectral satellite constellations provide global imagery with high spatial resolution and frequent revisit times. Planet's SuperDoves collect eight-band images at 3 m resolution with a 24-hour revisit time (Tu et al., 2022), while the SkySat C constellation captures four-band images with 0.5 m resolution at revisit intervals set by tasking contracts (Planet, 2023). In contrast, MAXAR's 16+ band Worldview-3 has a more traditional satellite design that offers spatial resolution of 0.3 m for panchromatic imagery, 1.24 m for visible and near-infrared (VNIR) imagery, and 3.7 m for shortwave infrared (SWIR) imagery (Longbotham et al., 2015). Other emerging systems offer moderate spatial resolution, but in the hyperspectral domain, including Planet Tanager (30 m, 420 bands; Planet, 2024), Kuvaspace Hyperfield-1 (25 m, 150 bands; Kuvaspace, 2024), PIXXEL (5-10 m, 250 bands; Petropoulos et al., 2024), Orbital Sidekick GHOSt (8 m, 500 bands; Sanders et al., 2024). In the thermal domain, Hydrosat's 16 constellation promises thermal infrared imagery targeted for agricultural use at 30 m spatial resolution (Lalli et al., 2022).

## Data pre-processing and analysis

To measure plant diseases using optical sensing, the data requires pre-processing (Bioucas-Dias et al., 2013; Aasen et al., 2018) and extraction of disease measures (Behmann et al., 2015; Verrelst et al., 2019). The raw signal acquired by a sensor must be converted to a meaningful biophysical quantity, e.g., surface reflectance (for multispectral imaging [MSI] and HSI; Daniels et al., 2023) or temperature (for TIR; Messina and Modica, 2020), via
radiometric calibration (Sterckx et al., 2019, 2020). To convert hyperspectral imagery into surface reflectance, it is essential to measure irradiance (the amount of incoming sunlight) at the time of image capture. To achieve this, irradiance should be recorded simultaneously with the imagery. This signal conversion should incorporate corrections for both the sensor and the local environmental conditions. Further, plant canopies can have different patterns of sunlit versus shaded, depending on solar and view geometries. This can confound analyses when multiple images captured at different times are stitched together (mosaicking; Ghosh and Kaabouch, 2016; Gómez-Reyes et al., 2022) or compared, although bidirectional reflectance distribution function (BRDF) approaches can correct for these effects (Collings et al., 2010; Queally et al., 2022). Terrain slopes may also distort the images, in which case topographic corrections are needed (Soenen et al., 2005; Vreys et al., 2016a, 2016b). Open-source packages are available which implement BRDF and topographic corrections (e.g., Chlus et al., 2023). For high-altitude platforms, light travels large distances, making atmospheric correction essential (Bioucas-Dias et al., 2013; Sterckx et al., 2016). This can be done by inverting radiative transfer models (RTM; Verhoef and Bach, 2003). In HSI, single pixels can contain spectra from different “pure materials”, or endmembers (e.g., soil, vegetation and shadow; Galvan et al., 2023), and spectral unmixing can tease out the spectra of individual endmembers for each pixel (Bioucas-Dias et al., 2012; Gu et al., 2023). Each pixel also needs to be attributed to a spatial location by georeferencing (Aasen et al., 2018), which may require ground control points (GCP), inertial measurement units, global positioning systems (GPS) or a combination of these (Bryson et al., 2010; Turner et al., 2014). When multiple sensors are used, their spatial co-registration is desirable (Scheffler et al., 2017). Several studies offer examples of standardisation and assessment of reliability of the data acquired using multispectral and hyperspectral sensors in controlled environments (Paulus and Mahlein, 2020), via ground-based measurements (Detring et al., 2024) and on-board UAV platforms (Aasen et al., 2018).

After data pre-processing, meaningful disease measures must be extracted, such as disease presence/absence, incidence or severity. To capture disease presence/absence or distinct qualitative classes of disease intensity (nominal scales; Bock et al., 2020), classification methods need to be used, whereas to capture quantitative measures of disease (e.g., incidence or severity), regression methods are more suitable. This can be done using parametric regression, machine learning (ML), RTM (see Challenge Ai below), or a combination of these methods (Verrelst et al., 2019). A range of ML approaches have gained particular prominence because of their capacity to handle complex, high-dimensional datasets (Behmann et al., 2015), including penalised linear regression (e.g., partial least squares regression; Geladi and Kowalski, 1986), kernel-based methods (e.g., support vector machine; Tuia et al., 2011), decision trees (e.g., random forest; Belgiu and Drăguț, 2016), and artificial neural networks (especially, deep learning; Yuan et al., 2020; Osco et al., 2021, Ispizua et al., 2024). Each of the ML approaches mentioned above can be formulated as a classification or a regression method. Further, in ML-based image analysis, we can train ML models to detect objects of certain types within images (e.g., diseased plants or fungal fruiting bodies), or perform image segmentation, where we subdivide an image into multiple regions, according to certain criteria (e.g., to separate diseased leaf areas from healthy leaf areas). We mainly focus on supervised ML that requires reference measurements of disease to be used as training and testing datasets but consider self-supervised ML that requires minimal reference measurements in Challenge Bi below.

## Current state-of-the-art in optical sensing of plant diseases

Several studies have reported plant disease measurements using various combinations of platforms and sensors across a range of spatial and temporal scales. For example, ground-based hyperspectral radiometers were used to detect and quantify septoria tritici blotch in diverse wheat cultivars (Yu et al., 2018; Anderegg et al., 2019). Further examples include detection and quantification of yellow (stripe) rust in wheat using UAV-based multispectral
(Su et al., 2018, 2019) and hyperspectral imaging (Guo et al., 2021), and hyperspectral imaging using both a ground-based vehicle and UAVs (Bohnenkamp et al., 2019a). Wheat blast has been quantified using UAV-based multispectral imaging (Gongora-Canul et al., 2020). Several UAV-based studies reported quantification of potato late blight using RGB imaging (Sugiura et al., 2016), multispectral imaging focusing on quantifying low severities (Franceschini et al., 2019), and detection of the disease using hyperspectral imaging (Shi et al., 2022). Tar spot disease in corn has been quantified with the help of ground-based RGB imaging (Lee et al., 2021; Lee et al., 2025), UAV multispectral imaging (Oh et al., 2021; Zhang et al., 2023) and a combination of multispectral and thermal imaging (Loladze et al., 2019). Ground robotics and rovers that automate side and lower canopy disease data acquisition offer a promising complement to aerial imaging (Liu et al. 2022a, 2022b, 2023).

**Box 1. Aerial avengers: remote sensing of *Xylella fastidiosa* on olives**

The vector-borne, xylem-limited bacterium *Xylella fastidiosa* causes serious diseases in a range of cultivated and wild plants, including Pierce's disease in grapevines and variegated chlorosis in citrus (EFSA, 2021). In 2013, the first report of *X. fastidiosa* in the European Union came from Italy (EFSA, 2013), where the pathogen was recognized to cause olive quick decline syndrome (OQDS, Martelli et al., 2016). OQDS has subsequently killed millions of olive trees in southern Europe (Bajocco et al., 2023), with reports now coming from several EU countries. Nevertheless, remote sensing of OQDS represents an inspiring success.

Substantial reference datasets have been collected for OQDS by quantitative polymerase chain reaction (qPCR; Harper et al., 2010) assays and *in situ* inspections, and linked to aircraft (Zarco-Tejada et al., 2018; 2021) and satellite (Hornero et al., 2020) remote sensing measurements. Combining results from visible to near-infrared HSI and TIR imaging sensors onboard piloted aircraft, Zarco-Tejada et al. (2018) detected OQDS symptoms in individual olive trees, often before they were visible to the naked eye. Camino et al. (2021) extended this approach with images in the shortwave infrared region, and showed how linking to dispersal processes from an epidemiological model could improve detection accuracy of *X. fastidiosa* in almonds at a pre-visual stage. Nevertheless, the confounding physiological effects caused by vascular pathogens and water stress in olive and almond required further work to reduce the detection of false positives. The evaluation of a wide range of spectral plant traits quantified from airborne hyperspectral and thermal images across host species (olive vs. almond) and across vascular plant pathogens (*X. fastidiosa* vs. *Verticillium dahliae*, a soil-borne pathogen that causes analogous symptoms) demonstrated that there are specific spectral-based traits for each plant species and pathogen (Zarco-Tejada et al., 2021; Poblete et al., 2021). Accounting for distinct spectral plant traits associated with the dynamics of water-induced stress improved early and pre-symptomatic disease detection (Zarco-Tejada et al., 2021). While detection of middle and advanced stages of OQDS development was reasonably successful using high-resolution multispectral satellite imagery, a critical conclusion is that the early (i.e., pre-visual) detection of *X. fastidiosa*- and *V. dahliae*-induced symptoms required a combination of HSI and TIR imaging from aircraft or UAV at high spatial resolutions (40-60 cm) to capture pure tree crowns (Poblete et al., 2023).

However, transferability of spectral signatures of OQDS to other olive-growing regions, and to other host species (e.g., coffee, citrus, grapevines) is an outstanding challenge. Remote sensing may be particularly suited to the slower dynamics of vascular wilt disease progression in trees compared to annual crops. Trees are larger and persist for longer than annual crop plants in a fixed spatial location, making the multitemporal monitoring of orchards at the required resolution and frequency technologically and operationally feasible. This means higher temporal resolutions and quicker turn-around processing times are required to achieve similar success in optical sensing measurements of annual crop diseases.

For some pathogens at certain spatial scales, it is now firmly established that visible to shortwave infrared (VSWIR) imaging spectrometers mounted on piloted aircraft (e.g.
AVIRIS-NG; Chapman et al., 2019) are capable of pre- and post-symptomatic disease detection (Zarco-Tejada et al., 2018, 2022; Hornero et al., 2021; Sapes et al., 2022; Romero Galvan et al., 2023). Satellite data have been used to map and monitor host plants across large areas (e.g., citrus in China; Xu et al., 2021), and to detect both systemic (e.g., Huanglongbing in citrus; Li et al., 2015) and localised (e.g., foliar grapevine downy mildew; Kanaley et al. 2024) diseases. More recently, optical satellite data have been used to track the spread of rice blast, and ground-based hyperspectral reflectance used to verify the satellite-derived predictions (Tian et al., 2023).

We highlight two research programmes that have achieved encouraging success in sensor-based disease detection and/or measurements in two contrasting pathosystems (systemic vs. localised): Xylella fastidiosa in olives (Box 1; a xylem-limited bacterial pathogen of a woody perennial crop) and Cercospora beticola in sugar beet (Box 2; a foliar fungal pathogen of an annual field crop). We note that the set of examples we have identified above is far from being complete. Many studies have achieved high accuracies of disease detection/quantification. However, with a few exceptions (e.g., Box 1), investigations have been conducted for a single disease in the absence of abiotic stress, and often in a single location. It is not clear whether the sensing signatures derived from these studies would be robust with respect to presence of other biotic and/or abiotic stresses (Challenge Aii), and to what extent the outcomes would be transferable to other host genotypes or other geographic locations (Challenge Aiii).

## Box 2. Fifteen years of optical sensing of Cercospora leaf spot in sugar beet

Cercospora leaf spot (CLS), caused by the ascomycete *Cercospora beticola* (Sacc.), is a serious threat to sugar beet production worldwide (Weiland and Koch, 2004; Rangel et al., 2020). This hemibiotrophic pathogen causes characteristic leaf spots with a reddish-brown border and a necrotic centre. Under favourable conditions, entire leaves become necrotic, causing reductions in the photosynthetically active canopy. Yield losses can reach 50% in regions with high disease pressure (Shane and Teng, 1992).

Thanks to intensive research during the last 15 years, clearly defined symptoms and the dicotyledonous growth with flat leaves of the host plant, *C. beticola* is now established as a model organism for plant disease detection using spectral sensors (Ruwona and Scherm, 2022). Diverse studies have characterised and detected CLS at different scales, from the microscopic (Leucker et al., 2016; 2017), to the tissue scale (Mahlein et al., 2012; Arens et al., 2016), to the leaf (Mahlein et al., 2010) and single plant scale (Günder et al., 2022). HSI with high spectral and spatial resolution in the visible, NIR and SWIR ranges provided high-quality data sets of reflectance and transmittance complemented with reference data from visual monitoring or analytics. Studies under controlled conditions provide basic knowledge on spectral characteristics of the disease (Mahlein et al., 2010), insights into sporulation and lesion phenotyping (Leucker et al., 2016; 2017), have linked disease aetiology to biochemical and structural processes (Arens et al., 2016; Mahlein et al., 2012) and permitted early detection before visible symptoms (Arens et al., 2016; Rumpf et al., 2010). Early studies addressed the differentiation of CLS from other foliar diseases such as sugar beet rust or powdery mildew and for the first time, disease specific spectral vegetation indices were developed (Mahlein et al., 2013). Due to recent innovations in robotics and the increasing availability of UAVs and spatially highly resolved RGB or multispectral cameras, these studies are now complemented by field scale studies on monitoring and detection of CLS (Barreto et al., 2023; Ispizua et al., 2022). Remote sensing using UAVs was successfully used for phenotyping of tolerant and resistant varieties (Görlich et al., 2021; Ispizua et al., 2022) and for extracting disease incidence and severity for decision making in integrated pest management.

The progress and knowledge gained in detecting CLS are likely to be useful for other host pathogen systems, because similar experimental approaches and data analysis pipelines are expected to work for a range of foliar fungal pathogens of field crops.
# Epidemiological modelling

## Data- versus process-based models

In categorising model structure, a key distinction is between data- and process-based models (Madden, 2006). Data-based models (aka empirical or correlative or statistical models; Gonzalez-Dominguez et al., 2023) are driven entirely by data, and do not attempt to capture or track biological mechanisms underpinning disease or disease risk. This class of model has a long history, with mathematical and statistical methods becoming increasingly complex. Current work often emphasises models including complex non-linear responses and/or multiple predictor variables (Shah et al., 2019), as well as statistically sound treatments of different types of measurements and their associated error structures (Garrett et al., 2004; Madden et al., 2007). Promising recent developments echo trends in epidemiology more broadly (Li et al., 2017) to develop techniques for combining multiple predictions from ensembles of models (Shah et al., 2021), and to account for and weigh different sources of evidence using Bayesian analysis and decision theory (Hughes, 2017).

Data availability is often a limiting factor for data-based models (Madden, 2006). This makes linking with optical sensing attractive, as it increases the volume, range and scope of data available for model parameterisation and validation. In turn, these expanded datasets enable the direct application of recent developments in machine learning to disease prediction. Although some recent studies have shown the potential of machine learning for plant disease prediction (e.g., Skelsey, 2021; Xu et al., 2018; Hamer et al., 2020; Martinetti and Soubeyrand, 2019), applications have so far been predominantly focused on data analysis for disease detection (Gobalakrishnan et al., 2020; Xie et al., 2022) and/or quantification (Anderegg et al., 2019; Oh et al., 2021; Barreto et al., 2023; Leclerc et al., 2023; Zhang et al., 2023; Lee et al., 2025).

## Process-based models

Process-based (or mechanistic) models instead aim to represent the biological basis of disease epidemics, focusing on the dynamics of disease in time and perhaps space (Madden, 2006). The dominant paradigm is compartmental modelling, an approach also widely adopted for diseases of animals and humans (Keeling and Rohani, 2008). Compartmental models divide a host population into mutually exclusive classes based on disease status. Levels of complexity vary, but the most common formulation distinguishes healthy and infected tissue, with a further partitioning of infected tissue into pre-infectious, infectious and post-infectious. In plant disease modelling this is often referred to as the H-L-I-R (Healthy-Latent-Infected-Removed) model (Madden et al., 2007), which - perhaps unhelpfully - obscures links with work on S-E-I-R (Susceptible-Exposed-Infected-Removed) models for pathogens of other host taxa (Keeling and Rohani, 2008). For plant diseases, the unit of interest tracked by a compartmental model is often the individual host plant, although host tissue can be tracked at smaller (e.g., organs such as roots or leaves, or infectible sites) or larger scales (e.g., entire fields or farms, or even counties/states), depending on the scale at which predictions are required.

Much work using compartmental models is theoretical, aiming to develop strategic understanding, and therefore not explicitly tied to a single system. The focus is on understanding broad principles relevant to a class of pathosystems without detailed reference to any single pathosystem. Often the key output is an improved understanding of epidemiological factors promoting invasion and persistence of pathogens (Gilligan and van den Bosch, 2008). Much work has also focused on how crop diversification affects disease dynamics, particularly for cultivar mixtures (e.g., Mikaberidze et al., 2015; Clin et al., 2022) and intercropping (Allen-Perkins and Estrada, 2019; Levionnois et al., 2023). Other theoretical work focuses on evolution and/or dynamics of adapted pathogen strains, for
fungicide resistance (van den Bosch et al., 2013; Mikaberidze et al., 2014; Mikaberidze et al., 2017; Taylor and Cunniffe, 2023a, 2023b; Corkley et al., 2025a, 2025b), resistance-breaking pathogens (Watkinson-Powell et al., 2020; Rimbaud et al., 2021; Zaffaroni et al., 2024a, 2024b), or both simultaneously (Carolan et al., 2017; Taylor and Cunniffe, 2023b). Yet other work has concentrated on complex interactions, e.g., in the context of climate change (e.g., Jiranek et al., 2023), interactions between different pathogens (e.g., Allen et al., 2019; Hamelin et al., 2019), between pathogens and their biological control agents (e.g., Jeger et al., 2009; Cunniffe and Gilligan, 2011), and between pathogens and their vectors (e.g., Donnelly et al., 2019; Falla and Cunniffe, 2024). Socio-economic implications of epidemics are explored by linking economic analyses or game theory with simpler models of exponential growth of disease (van den Bosch et al., 2018; van den Bosch et al., 2023) or with full compartmental models (Murray-Watson et al., 2022; Murray-Watson and Cunniffe, 2022, 2023; Mikaberidze et al., 2023; Hilker et al., 2024). Several studies have incorporated plant physiological processes into epidemiological models (e.g., Précigout et al., 2017). A final theme is the use of compartmental models to understand factors promoting disease detection (e.g., Parnell et al., 2015; Parnell et al., 2017; Lovell-Read et al., 2022) and control (e.g., Bussell et al., 2018; Russell and Cunniffe, 2025). Since underpinning compartmental models can easily be cast in stochastic as well as deterministic forms, work of this type often now also explicitly considers the risk of disease outbreaks (or, equivalently, the risk of failure of control) (Thompson et al., 2020).

## Using process-based models to make predictions and/or assess disease management

Process-based models can also be used to make predictions - in time and in space - for a given disease (Cunniffe and Gilligan, 2020). Similarly to data-based models, many process-based models target a single location - or set of distinct locations, with no consideration of the flow of inoculum between them - focusing on how aspects of the abiotic environment drive rates of epidemiological processes (see Gonzalez-Dominguez et al. (2023) for a recent review). Note that, despite the commonality of approach with compartmental models, in plant pathology such models are - arguably unhelpfully - often framed as "simulation models" (Savary and Willocquet, 2014) and presented in terms of visual systems dynamics modelling languages (Costanza and Voinov, 2001), although we should note these models can be readily translated into differential equations or discrete maps (Willocquet et al., 2020). Other process-based models, particularly when applied to emerging or invading pathogens, make predictions of spatial spread of a particular named pathogen across a region through a landscape of hosts susceptible to disease, considering the effects of particular disease detection and control strategies (see Cunniffe and Gilligan (2020) for a review).

For applications to spatial spread, underpinning models must consider flow of inoculum and so disease transmission between locations. Although network approaches have been promoted (Jeger et al., 2007; Shaw and Pautasso, 2014; Garrett et al., 2018), the large number of parameters that would need to be fitted, mean that full network-based models of dispersal tend not to be explicitly linked to data. Detailed spatial predictions instead tend to use the abstraction of the dispersal kernel, an idea used very widely in ecology more broadly (Nathan et al., 2012), to capture spatial dependencies via a parameterised functional form (reviewed in the context of plant disease epidemiology by Fabre et al. (2021)). The challenge is then to parameterise dispersal kernels and infection rates from (often very restricted) disease spread data at different spatial scales. Broadly speaking, three distinct approaches are used to characterise dispersal kernels: i) measurements of empirical disease gradients (Madden et al., 2007) in an experimental setting, using fungicide or other treatment to ensure there is only a single round of dispersal; ii) using a separate detailed model of dispersal parameterised to capture the underlying mechanism of spread; or iii) inferring the likely dispersal kernel from within a range of possibilities in process-based models using statistical approaches based on successive spatial snapshots of the pattern of disease (Cunniffe and Gilligan, 2020). Several studies have estimated dispersal kernels from disease
gradients, but only at distinct geographic locations and often within individual crop fields (e.g., Rieux et al., 2014; Mikaberidze et al., 2016; Karisto et al., 2022; Karisto et al., 2023). Explicit models of the dispersal process tend to be applied over large spatial scales, most often via computationally demanding spore trajectory simulations for wind-borne spread (Schmale and Ross, 2015; Meyer et al., 2017; Gilligan, 2024). Process-based models can be fitted using various statistical methodologies, ranging from simple least-squares or maximum likelihood techniques (Cunniffe and Gilligan, 2020) to more complex Bayesian methodologies based on likelihood functions and data augmentation (Gibson and Austin, 1996; Papaïx et al., 2022). For successful examples of doing so, see e.g., Soubeyrand et al. (2008), Cunniffe et al. (2014), Neri et al. (2014), Parry et al. (2014), Adrakey et al. (2017, 2023) or Nyugen et al. (2023). Some studies explicitly couple process-based and data-based approaches in the framework of state-space modelling or mechanistic-statistical modelling by defining a model of the observation process conditional on the model of the epidemiological dynamics and deducing from this construction a Bayesian inference scheme (Soubeyrand et al., 2009; Pleydell et al., 2018; Papaïx et al., 2022; Abboud et al., 2023; Saubin et al., 2024). When likelihoods are intractable or very complex, as can often be the case when fitting stochastic models at the landscape scale, the current vogue relies on Approximate Bayesian Computation via repeated simulation and the use of a distance between summary statistics computed from the observed and simulated data sets as a proxy for a formal likelihood function (Minter and Retkute, 2019; Godding et al., 2023).

The current state-of-the-art in epidemiological modelling often involves use of parameterised stochastic compartmental models to predict how epidemics will spread in time and space. While some work focuses on spread within fields (e.g., Mikaberidze et al., 2016; Karisto et al., 2022; Karisto et al., 2023) or relatively small production sites (e.g., Cunniffe et al., 2014; Parry et al., 2015; Craig et al., 2018), recent applications of these models tend to focus on large spatial scales and link spread modelling to optimisation of disease detection (e.g., Mastin et al. (2020) for citrus greening and Martinetti and Soubeyrand (2018) for *Xylella fastidiosa*) or disease control (e.g., Cunniffe et al. (2016) for sudden oak death in California, Ellis et al. (2025) and Nyugen et al. (2023) for citrus greening, Godding et al. (2023) for cassava viruses in sub Saharan Africa). The huge increase in availability of spatial data on sets of locations infected over time promised by optical sensing is incredibly attractive for predictive use of process-based models.

# Opportunities in linking epidemiological modelling and optical sensing for plant disease

## How can optical sensing inform epidemiological modelling?

**Oi) The vast amounts of data generated by optical sensing will greatly benefit the development of epidemiological models.**

The predictive power of any epidemiological model is limited by the amount and quality of data used for parameterisation. Similarly, our confidence in model robustness depends on the range of data used for validation (Challenges Bi-Biii). Traditional methods to acquire plant disease data, whether in controlled environments or under field conditions, can be costly and often require expert assessors. Optical sensing promises to generate disease data at hitherto impossible spatio-temporal scales and resolutions, while allowing a much wider range of environmental conditions and locations to be sampled, including locations that are inaccessible from the ground. Increased data availability enhances the reliability and usefulness of any data-based disease model, meaning less extrapolation is required for model use in real-world settings (Madden, 2006).
Optical sensing also has the potential to improve parameterisation of process-based models. More finely resolved spatio-temporal data then leads to more accurate parameter estimation and/or better prospects for (practical) identifiability of parameters (Cunniffe et al., 2024).

Similarly, model selection (and model averaging) is often expected to become more powerful with an increased amount of data (Kuparinen, et al., 2007). High revisit frequencies allow the amount or spatial pattern of disease to be assessed repeatedly, leading to an improved understanding of disease dynamics over time. Focusing on spatial dynamics in particular, optical sensing would enable disease measurements over much denser and larger grids of locations (e.g., as disease gradients) with higher numbers of treatments and replicate experimental plots than is feasible by conventional visual assessments (e.g., Sackett and Mundt, 2005). This would dramatically improve our capacity to quantify pathogen dispersal and reproduction via estimation of dispersal kernels (Soubeyrand et al., 2007; Farber et al., 2019; Karisto et al., 2022, 2023) together with strengths of infected source areas producing spores or other infectious propagules (Bousset et al., 2015), and basic reproduction numbers (Segarra et al., 2001; Mikaberidze et al., 2016; van den Bosch et al., 2024).

Conducting such experiments characterising disease gradients across diverse geographic locations would provide (in conjunction with weather data) detailed knowledge of how pathogen dispersal and reproduction depend on the three aspects of the disease triangle (i.e., the genotypes of the host and the pathogen as controlled by the experimental design, and the environmental variables; Madden et al., 2007). This improved knowledge concerning dispersal and reproduction can then feed into spatially explicit process-based models, increasing their power to predict epidemics and evaluate disease management approaches.

**Oii) Non-destructive and objective disease quantification by optical sensing will overcome difficulties and bias in human scouting and rating**

However, optical sensing can do more than simply increase the volume of data. Certain diseases have symptoms that are difficult to recognise or distinguish from other diseases, or from more general signs of abiotic/biotic stress (Challenges Aii and Aii). It can also be quite difficult and time consuming even for trained assessors to unambiguously assess severity, i.e., to measure the level of infection within a single host or group of hosts. These factors introduce subjectivity and bias into human scouting and rating (Nita et al., 2003; Nutter et al., 2006; Bock et al., 2020), in turn affecting the reliability of epidemiological models using these types of data for their parameterisation, albeit in a way that is seldom, if ever accounted for in the analysis (Challenge Ciii). Although lab-based molecular diagnosis is recognised for its sensitivity, accuracy, and reliability (Venbruz et al., 2023), it is typically destructive, requiring plant tissue to be removed for assessment, and may not be cost-effective (Mastin et al., 2019). Non-destructive, objective disease quantification - as would be generated by methods based on optical sensing - is therefore very valuable, even when sensors are not deployed over large spatio-temporal scales and resolutions, provided that the sensing and analytical approaches allow for correct disease diagnosis and quantification (Oh, et al., 2021; Zhang et al., 2023).

**Oiii) Host maps and comprehensive environmental characterization provided by optical sensing will improve landscape-scale models**

For landscape-scale spatial process-based models (Cunniffe & Gilligan, 2020; Fabre et al., 2021), remote sensing also offers various classes of data that might be used directly as a model input, rather than as a source of data for model fitting. An important example is information on the location of susceptible hosts. Although maps have been produced for certain major crops (You et al., 2014), location data are often either unavailable or are only available at low spatial resolution, or perhaps even for other host species entirely. In such cases, host maps used as model inputs tend to be based on statistical inference (e.g., Meentemeyer et al., 2011; Ellis et al., 2025), losing small-grained but relevant features such as sizes and relative locations of individual fields or orchards. Since remote sensing can now
reliably distinguish between different plant species (Fassnacht et al., 2016; Kordi and Yousefi, 2022; Ashourloo et al. 2022) and in some cases even between different sub-species/varieties/cultivars (Rauf et al., 2022; Lyu et al., 2024; Bégué et al., 2024), the level of biological realism in host maps could be increased. In the context of landscape epidemiology, locations of potential inoculum reservoirs might be particularly important (Plantegenest et al., 2007), both populations of the same host species and/or of cultivated/wild alternative hosts (Emery et al., 2021; Morris et al., 2022), or crop residues from previous growing seasons (e.g., piles of potentially infectious tubers for potato late blight, or pathogen-harbouring standing stubble from previous host crops). Such reservoirs force epidemics, as well as potentially providing refugia for pathogens to persist between growing seasons/years. However, the potential for proliferation of species-specific parameters in epidemiological models would need to be carefully considered (Cunniffe et al., 2015). Models could also better reflect spatial variation in host plant density for a given growing season if they used real-time information from optical sensing, rather than - as present - resorting to use of historical data or simple functions to parameterise growth over time. This would also allow time-dependent ecophysiological information on plant status to inform epidemiological models.

Other biotic/abiotic factors affecting disease can be characterised by optical sensing (Dlamini et al., 2019), and so in turn could be included in disease models. This includes information on landscape topography, soil structure and water availability, as well as the phenology of the host crop. An example of integrating phenological information with modelling is a regional-scale Susceptible-Exposed-Infected-Removed model of Fusarium head blight (Xiao et al., 2022). There is also real-time information that can be remotely sensed and contribute to prediction when epidemiological models are used predictively for short-term forecasting (Gilligan, 2024). Exciting examples include sets of currently infected locations (e.g., Allen-Sader et al., 2019), definitive confirmation of whether previously scheduled control by host removal has in fact occurred (Carnegie et al., 2023), and real time information on meteorological driving variables as sensed by Global Navigation Satellite Systems (Bianchi et al., 2016).

## How can epidemiological modelling inform optical sensing?

### Oiv) Model outputs will help to improve disease classification and interpretation of optical sensing data

Arguably most importantly, epidemiological modelling offers a mechanism to improve the accuracy of disease measurement via optical sensing. Optical sensing of plant disease often has a classification task at its core, in which a binary decision is made about whether a given location (i.e., a position in an image) is diseased or not. Outputs of epidemiological models could improve this classification by providing the classifier with additional relevant information. For example, positive confirmation from simple weather data-based models to estimate the risk of disease at a given location could provide greater confidence that an ambiguous signal from optical sensing in fact corresponds to disease.

There is also useful information in the spatial and spatio-temporal pattern of disease that can be used in interpreting optical sensing data. It is well known that plant diseases are clustered at a range of scales: accordingly, much statistical modelling work in plant disease epidemiology concentrates on quantifying these relationships (Madden et al., 2007; Madden et al., 2018). The corollary is that any location is more likely to be infected if its neighbours are also infected. This idea is at the core of the methodology used by Camino et al. (2021) for X. fastidiosa in almond orchards (Box 1) in which predictions from a static probabilistic model of disease risk (Parnell et al., 2011) were used to ascribe a probability of infection to different trees via a model based on an exponential dispersal kernel. These predictions were then combined with remote sensing results to come to an overall prediction of infection.
status on a tree-by-tree basis. Also more complex, dynamic epidemiological models could be
used in this framework, closely coupling interpretation of optical sensing data to the explicitly
probabilistic predictions in space and time made by a process-based epidemic model.
Similar improvement may be achieved by using convolutional neural networks (CNN) to
extract disease information from optical sensing data: CNNs use convolutional kernels with
combination pooling to extract local features, potentially allowing the spatial topology and
geometry of optical sensing data to be incorporated into predictions. However, in contrast to
process-based models, the parameters of CNN lack biological interpretation and hence this
approach would not provide as much insight into the processes driving epidemics.

Ov) Models will help to decide where, when and how to deploy sensors, including guiding flight routes in near real-time for surveillance

The other way in which epidemiological modelling might be useful for optical sensing is in
setting where, when and how sensors should be deployed (Mahlein et al., 2024). Optical
sensing is particularly promising for early detection, a key constraint in the “controllability” of
an infectious disease outbreak (Fraser et al., 2004). The possibilities here range from being
able to detect pathogens in near real-time over hitherto unimaginable spatial scales
(Challenge Aiii), to “anomaly detection”, i.e., characterising spectral signatures associated
with healthy plants and using any deviation from this to trigger ground scouting or other
disease management (Challenge D). Real-time information could also be used to better
guide disease surveillance, e.g., incorporating epidemiological models into automated flight
route planning for UAVs or planning satellite surveillance patterns. In principle, each
successive sample could then be taken from areas in which knowledge is weakest, and so
from which confirmation of disease positives (or negatives) would be most useful (see Cook
et al., 2008 and Parisey et al., 2022 for examples of this broad idea). Of course, reliably
detecting disease is only the first step in disease management, and many disease controls
are applied reactively in response to detections of disease. Much modelling work focuses on
how optimal patterns of reactive control can be identified based on observation patterns of
disease until a given time (Hyatt-Twynam et al., 2017; Bussell et al., 2018). This raises the
possibility of a moveable platform that combines optical sensing with disease control, such
as a robotic ground-based vehicle that operates within a greenhouse or in the field (e.g.
Oberti et al. 2016). This has obvious applications in precision agriculture (Yang, 2020) and
would echo well-publicised parallel developments for automatic weed detection and
destruction mounted on tractors and other cultivation equipment (Zhang et al., 2022).

We summarise the opportunities presented above in Table 2. There, we list the relevant
plant traits and features that could be estimated via optical sensing and indicate how this
estimation could aid epidemiological modelling, and how epidemiological modelling could aid
the estimation.

A) Challenges in identifying a particular disease from optical sensing data

Ai) Immature understanding of disease mechanisms underpinning spectral responses

Understanding spectral plant traits associated with disease is clearly important (Mahlein et al., 2012; Zhang et al., 2012; Zarco-Tejada et al., 2018; 2021). However, and with some exceptions, we lack knowledge of mechanisms by which disease-induced alterations in plant physiology and biochemistry translate into detectable variations in spectral signatures (Oerke, 2020). Additionally, the degree of conservation of spectral responses across plant genotypes and/or environments is unclear (Terentev et al., 2022). Other domains of spectral biology suggest the more highly conserved the underlying processes, the more likely their
associated spectral features will be too, as shown for hyperspectral reflectance of healthy leaves across 544 plant species (Meireles et al., 2020). Divergent spectral pathways associated with shared physiological symptoms have been disentangled recently between major fungal foliar diseases of wheat (Bohnenkamp et al., 2021) and sugar beet (Mahlein et al., 2012; Brugger et al., 2023) in controlled environments, and in other pathosystems in the field (Fallon et al., 2020; Gold et al., 2019a; 2019b; 2020; Zarco-Tejada et al., 2021). Studying differences and similarities in spectral responses for pathogens affecting plant health via different underlying mechanisms is therefore essential.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Summary of opportunities and challenges in integrating optical sensing and epidemiological modelling.</figcaption></figure>

A promising methodology to distinguish plant disease from other stress responses is based on plant functional traits, which has emerged as a unifying framework to understand natural and stress-induced variation in vegetation (Wright et al., 2004; Ustin et al., 2004). Plant pathogens damage, impair and/or alter plant function, and their impacts on plant traits can be sensed both before and after disease symptoms appear. Methods to quantify functional traits from optical sensing data can be based on either statistical modelling (e.g., partial least squares regression [PLSR], random forests or Gaussian process regression) or radiative transfer modelling (RTM). RTM allows leaf and canopy traits linked to plant physiological processes to be retrieved from spectra (Essery et al., 2008; Kattenborn and Schmidtlein, 2019), whereas PLSR iteratively transforms predictor (spectra) and response variables (traits) to create predictive models (Serbin and Townsend, 2020). Compared to empirical approaches based on single-band or vegetation indices, quantifying spectral traits linked to stress-induced biological mechanisms improves model accuracy and transferability (Camino et al., 2021; Hornero et al., 2021; Poblete et al., 2021; Zarco-Tejada et al., 2018). Machine learning further allows robust extraction of these traits from complex spectral data even under diverse conditions (Verrelst et al., 2019; Serbin and Townsend, 2020). Combining PLSR/RTM and machine learning should improve our ability to scale from controlled studies to the field (Challenge Aiii) and from foliar to spaceborne scales (Poblete et al., 2023; Zarco-Tejada et al., 2021).
### Aii) Spectral signatures are inherently variable and unknown for some pathogens

Many spectral signatures of plant diseases have been reported. However, a given plant pathogen in an identical environment can very often show different symptoms. For instance, symptoms of *Phytophthora* spp. on citrus depend on the tissue affected (i.e., root rot, fruit brown rot, gummosis of bark or twig desiccation) (Cacciola et al., 2007). Identifying specific signatures of pre-symptomatic infection remains particularly challenging (Gold et al., 2019b; Rumpf et al., 2010), requiring deep knowledge of the plant-pathogen interaction to determine physiological parameters that could be affected early in disease development. Diseases can also manifest differently depending on location (Calderón et al., 2014), pathovar (Gold et al., 2019b) and host genotype (Gold et al., 2019a; Surano et al., 2022), or depending on interactions between pathogen isolates and host genotypes (Kader et al. 2022), as well as when plant hosts experience abiotic stresses, such as nutrient deficiencies (Abdulridha et al., 2019) and water stress (Zarco-Tejada et al., 2021). Biotic stresses can also be confounding factors (Gold et al., 2020; Poblete et al., 2021), particularly in cases of co-infection by distinct pathogen species (Bohnenkamp et al., 2019b). Differentiating between above-ground symptoms of abiotic stresses and diseases is particularly challenging for soil-borne pathogens (Hillnhütter et al., 2011). Ontogenic resistance, as well as other effects of leaf age on spectral responses, may also play confounding roles (Chavana-Bryant et al., 2019). Anthropomorphic factors, e.g., mechanical damage and pesticides/fertilisers, may further mask spectral responses (Gambhir et al., 2024, Wang et al., 2022). Additional variation stems from interactions between these factors, as well as simply from the natural variability of agro- and natural ecosystems (Oerke, 2020).

Even setting aside significant but unavoidable complications caused by variability, optical spectral signatures of many pathosystems remain to be characterised. Of course, finding signatures may be intrinsically challenging for certain pathosystems. For example, shorter plant and pathogen life cycles may allow less time to characterise disease-associated signatures than for diseases in longer lived pathosystems, although in some pathogens with fast life cycles this might be easier due to a lack of significant asymptomatic infection. Other aspects of any given pathosystem - e.g., whether symptoms are exhibited on foliar or woody tissue, as well as the size/pigmentation of affected plant organs - also clearly play a role.

Spectral libraries cataloguing signatures across scales, diverse environments, conditions, host ages and species, stages of infection, and damage mechanisms (Boote et al., 1983) are sorely needed (Bohnenkamp et al., 2021; Zhu et al., 2023). This would allow us to investigate the transferability of spectral signatures given these potentially confounding factors.

### Aiii) Scaling from controlled to field conditions and from proximal to remote sensing

Spectral signatures also depend on choices of sensors, platforms and spatial/spectral resolutions, as well as lighting and exposure times, even under controlled conditions (see 'Current state-of-the-art'). Scaling to field conditions is therefore expected to be challenging. Additionally, signatures that are specific at the foliar scale are not necessarily most useful at the canopy scale (Calderón et al., 2013; 2014; 2015; Herrmann et al., 2018, Bohnenkamp et al. 2019b, 2021). If effective detection depends on expensive sensors, e.g., with high detection sensitivity across narrow bands in the SWIR, lack of access to such sensors may hinder scaling to the field. Using openly available earth observations from space agencies is appealing, with particular success for detecting defoliating insects (Dalponte et al., 2022), but other applications may be hindered by limited spectral and/or spatial resolution of the currently available satellite data.

In low to medium spatial resolution imagery (where the pixel size exceeds the size of the plant or plant unit of interest), it can be difficult to separate vegetation spectra from mixed
signals of soil background, shadows and understory vegetation (Hornero et al., 2020), although spectral unmixing techniques can disentangle spectral diversity at sub-pixel levels (Galván et al., 2023). Nadir (straight-down) view systems are valuable for capturing visible symptoms on upper canopies but are less useful for diseases developing primarily in the lower canopy (Abdulridha et al., 2020; Carlier et al., 2023, Kanaley et al., 2024). Spectral signatures can become distorted when scaling the measurements from the leaf scale to the canopy scale and systematic investigation of these changes is challenging (e.g., comparison of leaf versus canopy reflectance for cereals in the red edge region; Li et al., 2017). Although high-spatial resolution and multiangular remote sensing enhance disease detection across canopy layers, operational challenges arise for regional-scale monitoring (He et al., 2021; Zhang et al., 2023). Various factors can introduce uncertainties, including canopy complexity, atmospheric conditions, sensor calibration inaccuracies, and radiometric correction (Daniels et al., 2023; Delalieux et al., 2009; Tanner et al., 2022). In particular, bidirectional reflectance effects, influenced by solar illumination and viewing geometry changes, pose difficulties with data collected at different times of day, under varying lighting conditions, and across different canopy structures. However, as described above, BRDF (Collings et al., 2010; Queally et al., 2022) and RTM approaches (Hornero et al., 2021; Zarco-Tejada et al., 2018) may be able to correct for these effects.

## B) Challenges associated with data availability, quality and resolution in optical sensing of plant diseases

### Bi) Insufficient reference data

Optical sensing requires accurate reference measurements of disease for training, testing and validation (depending on the field, reference measurements are sometimes called “annotated data”, “labelled data” or “ground truth”). But as described in Opportunities above, such data are scarce, because they tend to be time- and resource-consuming to acquire. Visual assessments in the field can be cost-effective and under certain conditions can have high throughput, but yet require skilled evaluators and can also be prone to error, most often due to inherent variability (Nutter et al., 2017; Bock et al., 2021). Attention needs to be paid to training of assessors, standardisation of measurement protocols, data verification, normalization and calibration, and assessment of measurement uncertainties (Bock et al., 2021).

Crowd source annotation (e.g., like Pl@ntNet; Joly et al., 2016), in which data labelling or classification is outsourced to a large group of people, could become a valuable additional source of reference data, but also requires careful validation. Even with enhancements, visual assessment may overlook indicators not immediately apparent to the naked eye. Ideally, visual assessment should be confirmed by molecular laboratory analyses (Martinelli et al., 2015; Donoso and Valenzuela, 2018). This can be especially important for pathogens not easily recognised in the field, or when multiple pathogens cause similar symptoms (Abdullah et al., 2018) (Challenges Ai and Aii).

RGB imaging provides a potential source of reference measurements (Anderegg et al., 2019). The methodology has been developed to measure foliar diseases in major crops, e.g., septoria tritici blotch on wheat (Stewart et al., 2016; Karisto et al., 2018), tar spot on corn (Lee et al., 2021; Lee et al., 2025), as well as bean angular leaf spot, rice brown spot, wheat tan spot and soybean rust (Olivoto et al., 2022). However, with some exceptions, such as a recent study on red needle cast of pine (Fraser et al., 2022), acquiring RGB images of sufficient quality has thus far required destructive sampling and manual processing (e.g., Karisto et al., 2018; Lee et al., 2021; Zenkl et al., 2024) or non-invasive in-field imaging (Anderegg et al., 2024; Lee et al., 2025) of individual diseased leaves. This tends to be more resource consuming than visual assessments. A higher throughput will be achieved by
capturing close-range images from within entire canopies, but several challenges need to be overcome, including variable lighting, blur due to canopy movement for example from wind or UAV downdraft, and extraction of relevant image parts (Zenkl et al., 2024). Most existing RGB imaging methods are yet to be used to produce reference data for optical sensing. Calibration and optimisation for this specific purpose are therefore required.

Self-supervised learning (SSL) or foundation models may overcome insufficient labelled data in a different way (Wang et al., 2022; Moor et al., 2023; Culman et al., 2023). SSL models can be formulated as convolution neural networks or vision transformers (Han et al., 2023). First, an SSL model is “pre-trained” on a large, unlabelled dataset, ideally, capturing a wide range of conditions, according to automatically generated objectives rather than annotated data as in conventional ML (Zhao et al., 2023). In this way, SSL models can extract useful, abstract and generic high-level representations from unlabelled data (e.g., visual representations; Doersch et al., 2015). Next, the SSL models are trained for a specific task (i.e., “fine-tuned”) using a limited amount of labelled data (Bengar et al., 2021). Similarly, foundation models can be trained on broad sets of unlabelled data and apply information about one situation to another (Moor et al., 2023). Both approaches can therefore learn from large volumes of unlabelled data and this promises to improve model generalisability to unseen domains (Wang et al., 2022, Lan et al., 2022). However, it will be important to evaluate the outcomes to ensure accuracy.

## Bii) Repurposing data originally collected for other purposes

Data potentially valuable as reference data could be sourced from growers, agronomists or diagnostic clinics. However, disease severity is often not available, and geolocation is often absent. There can also be questions around reliability, as well as the willingness of stakeholders to engage and share data in a standardised format (Buhrdel et al., 2020). As described in Challenge Bi, above, severity is difficult to assess even for experts (Bock et al., 2022), and certain diseases can be challenging to distinguish from each other (Barbedo, 2016; Abdullah et al., 2018), as well as from other stressors, especially when they occur together. However, apps for disease identification/detection (e.g., Siddiqua et al., 2022) – deployed on smartphones and so automatically geolocated – are promising, as are phone surveys (Allen-Sadler et al., 2019). But the potential for bias in citizen science observations where public volunteers help to collect data (e.g. Baker et al., 2019) cannot be ignored. Another ever-growing source of data is social media/online news (Tateosian et al., 2023), the potential of which is highlighted by a system integrating internet media scraping into a predictive early warning system for wheat stem rust in South Asia (Smith et al., 2024).

At larger scales, global searchable repositories including CABI (2023) and EPPO (2023) collate presence-absence data for plant diseases. However, spatial scales are far too coarse, and temporal resolutions too low, for epidemiological modelling applications. Despite this, large-scale crop health assessments have been used with earth observation data, e.g., CIMMYT's multi-seasonal survey of wheat rusts (Pryzant et al., 2017). Data from long-term forest biosecurity and health surveys have also been used to validate identifying *Phytophthora pluvialis* from satellite imagery in New Zealand forests (Watt et al., 2024). For pathogens of crops, data from regulatory surveys of disease are sometimes becoming available (e.g., Turner et al., 2021 for cereal diseases in the UK), and large-scale participatory surveillance efforts involving growers/agronomists are also appearing (e.g., Bregaglio et al., 2022 for grapevine downy mildew in Italy). However, potentially highly valuable field trial data collected by breeding/agrochemical companies tends to remain siloed for commercial reasons. Other potentially useful regional-scale data sources include daily disease risk maps (e.g., Shah et al., 2014), particularly when informed by real-time spore trapping data (e.g., Fall et al., 2015), although methods to integrate probabilistic disease predictions with optical sensing are needed (as discussed in Opportunities above).
### Biii) Resolution and scales in time and space

Clearly, measurements should capture relevant scales in time and space to quantify traits of interest, which informs the choice of sensor-platform setups. As part of this decision-making process, host units of infection (e.g., individual plant organs such as leaves or inflorescences, or individual plants, or groups of plants) must be identified based on the pathosystem in question. However, this raises trade-offs. Ground-based platforms and UAVs can capture images down to millimeter-scale resolutions (e.g., Bohnenkamp et al., 2019), but only across a limited area. In contrast, measurements via aircraft and satellite platforms capture scales up to entire regions (e.g., Poblete et al., 2023; Galvan et al., 2023) or even continents (Kampe et al., 2010), but with lower resolution. A similar trade-off affects temporal resolution. With a fixed budget, any sensing platform can only be deployed a fixed number of times, requiring decisions over whether to sample densely over a limited time interval or more sparsely over a longer time (Mateu & Müller, 2012). Commercial satellites now capture near-daily images of the entire globe with spatial resolution of ≈3 m (e.g., Planet; Liu et al., 2012). Governmental satellites Landsat-8, Sentinel-2A, and Sentinel-2B together provide a global median average revisit time of 2.9 days (Li and Roy, 2017), with a spatial resolution of 10 to 30 m for the multispectral sensors. On the other hand, currently operational hyperspectral satellites (e.g., EnMAP) can provide high spectral resolution (224 contiguous narrow bands) over a wider spectral range (420-2450 nm) with a spatial resolution similar to Landsat-8 although revisit time is coarser at 4 days for off-nadir capture, and longer in nadir view mode (Chabrillat et al., 2024). Plant disease measurement projects need to adapt to these specific revisit times and other parameters of satellite imagery. Yet, the long-term, large-extent sets of satellite images will allow modelling of long-term trends in disease dynamics, which would simply not be available from other data collection methods.

Combining datasets acquired using different sensing platforms and technologies can help to overcome these limitations and tradeoffs in scales and resolutions (Berger et al., 2022), for example, via spectral and spatial unmixing (Delalieux et al., 2014). Multiple hyperspectral reflectance datasets acquired via both remote and proximal sensing have been merged to improve characterisation of uncertainties and transferability of estimates of functional plant traits (Singh et al., 2015; Cherif et al., 2023; Challenge Ai). Also, results of small-scale proximal sensing confirmed via collection of reference data at a small number of tightly monitored sites could be combined with large-scale remote sensing, for example satellite imagery, to lead to more expansive inferences (Camarretta et al., 2024).

However, integrating data from different sources can be complex (Wang et al., 2023; Sisodiya et al., 2023), particularly if some data are missing (Ekue-wei and Blackburn, 2018; Zhao et al., 2018). Different datasets might not be aligned in space and/or time and might use different formats. Data fusion, defined as "the process of combining data from multiple sources to produce more accurate, consistent, and concise information than that provided by any individual data source" (Munir et al., 2021), is a potential solution (Barbedo 2022; Ouhami et al., 2021). Data fusion techniques, some applied to agricultural problems for almost three decades (Solberg et al., 1994), include regression methods, spatial and temporal adaptive reflectance fusion model (STARFM)-like statistical methods, geostatistical tools, principal component analysis (PCA), Kalman filters and machine learning (Barbedo, 2022). However, persistent challenges hinder the widespread adoption of data fusion. These include data variability and representativeness, integration complexity, overfitting, unrealistic assumptions, demand for high-performance computing, economic and technological constraints, and socio-political factors (Barbedo, 2022). Data fusion should be used in conjunction with comprehensive model-data integration approaches to address the complexities and uncertainties inherent in plant systems data (Cui et al., 2024; Kofidou et al., 2023). In this context, data fusion might be developed in the framework of Bayesian hierarchical modelling (Bourgeois et al., 2012; Wang et al., 2018), allowing us to couple
multiple observation models - corresponding to different types of optical sensing data -
defined conditionally on a particular epidemic model.

Biv) Socio-economic constraints including regulatory barriers and privacy concerns

A lack of access to data may drive new power relations around data (Kos and Kloppenburg, 2019). Growers may lack the basic infrastructure required for measurements (Garske et al., 2021), may perceive the monitoring of their fields as revealing commercially sensitive data, or perhaps experience it as invasive in other ways (European Court of Auditors, 2020), or may not trust the interpretation of the data (Purdy, 2011). In some contexts, there are also concerns that data may be used for purposes other than those intended (Gardner et al., 2019; Kos and Kloppenburg, 2019). Privacy legislation varies by country (Maniadaki et al., 2021) further challenging the application of the technologies. To address these concerns, governments and international organisations should focus on improving data regulation and legislation, as well as digital literacy (Kos and Kloppenburg, 2019). Support by growers and other stakeholders might increase if efforts were made to communicate, to develop data sharing agreements, and to promote co-production approaches with them (Purdy, 2011; van Rees et al., 2022). Thanks to technological developments in sensors and platforms, optical sensing data relevant to plant diseases can now be acquired at a much lower cost than before. Also, data storage and computing facilities for data processing have become more affordable. All these factors promise to make commercial deployment of optical sensing more profitable (Weiss et al., 2020; Wolfert et al., 2017).

C) Challenges in linking optical sensing and epidemiological modelling

Ci) Compatibility between optical sensing data and epidemiological models

Optical sensing data may inform state variables of epidemiological models (e.g., susceptible, infected or symptomatic states), particularly when models are spatially explicit. But the spatial and temporal resolutions of the data must then match the spatial and temporal resolutions tracked by the model. Super-resolution methods can improve the spatial resolution of sensing data, at least to some extent (Wang et al., 2022), and signal processing methods (Li and Revesz, 2004; Yang and Hu, 2018) can be used to interpolate sensing data to achieve desired resolutions in space and time. However, handling high-resolution data may become computationally demanding. Statistical downsampling can be used if coarser resolution is needed (Atkinson, 2013). Optical sensors can be used to characterise aspects of plant physiology (e.g., photosynthesis or water relations) (Zhang et al., 2021), while these aspects are omitted by most current epidemiological models. However, integrating plant physiological processes into epidemiological models is an active area of research (e.g., Precigout et al., 2017), suggesting physiologically designed sensors will likely inform future epidemiological models. In general, statistical methods for spatio-temporal designs (Mateu and Müller, 2012) could be used to efficiently design plant disease monitoring via optical sensing for compatibility with epidemiological models, but these may require heterogeneous data acquisition across different spatial scales, meaning data fusion becomes challenging (see also Challenge Biii; Berger et al., 2022; Barbedo, 2022).

Cii) Using data assimilation methods for model fitting

A major opportunity for combining epidemiological models and optical sensing data is to
obtain estimates of epidemiological parameters. This problem is referred to as parameter
estimation or identification, or in some cases as inverse problems. Several methods are
available. As epidemiologists often already must tackle sparse and noisy data, they routinely
formulate suitable observation processes (e.g., zero-inflated) and methods (e.g. Markov
chain Monte Carlo [MCMC], likelihood, or nonlinear least-squares optimization) for inferring
parameters when potentially useful information is unavailable (Gibson, 1997, Soubeyrand et
al., 2014), in both frequentist and Bayesian statistical frameworks. Alternatively, model
parameters or states can be estimated using data assimilation (DA) (Asch et al., 2016;
Pandya et al., 2022). These methods may prove particularly suitable for fitting
epidemiological models to optical sensing data, because they have been adapted to handle
image data (Papadakis et al., 2008; Mang et al., 2020). DA is broader than parameter
estimation and is well-suited for sequential data acquisition, meaning model parameters and
predictions could be automatically updated as new image data are acquired. Finally, we can
draw inspiration from recent machine learning methods developed to solve DA problems in
physics (physics-based deep learning; Cheng et al., 2023; Thuerey et al., 2021), that are
increasingly used in epidemiology (Ye et al., 2025), to fit mechanistic epidemiological models
to optical sensing data.

### Ciii) Accounting for data uncertainty in epidemiological models

While optical sensing data offers new opportunities for epidemiological modelling, additional
uncertainties and errors will also be introduced. For example, environmental conditions (e.g.,
cloud cover, aerosol loading), can influence sensor measurements (Daniels et al., 2023).
The frequency of data acquisition can also vary (Challenge Biii), meaning sensors may fail to
capture important events such as early infections (Gold et al., 2019b; Rumpf et al., 2010).
Similarly, spatial heterogeneity in host topology and species/cultivar can only ever be
partially captured by optical sensing. Pre-processing techniques applied to raw data from
optical sensors (as described above in current state-of-the-art) may introduce further
uncertainties.

Following pre-processing and analysis, it is now established that optical sensing data can be
used to obtain point estimates of the spatial distribution of infections (e.g., Boxes 1 and 2).
However, predictions have two main sources of uncertainties. Firstly, prediction of disease
occurrence and severity from remote sensed data is subject to several, known and unknown,
potential confusions between biotic and abiotic causes (Challenge Aii). We note the types of
errors in optical sensing data may be different from those in reference measurements (e.g.,
human observations of symptoms, molecular detection) and this will require specific
treatment. Secondly, ML algorithms used for processing optical sensing data themselves
make errors. This may make epidemiological parameters as inferred from optical sensing
data either potentially unreliable or difficult to interpret (Leclerc et al., 2023). Furthermore,
there are challenges associated with intra-class variability (where it can be difficult to
establish a boundary between classes) and inter-class similarity (where the inherent
similarity between certain classes means that the class of an individual pixel can be difficult
to determine unambiguously) (Qin and Liu, 2022; Bi et al., 2021).

Both types of error should be considered for forward predictions from epidemiological
models. Promising methods have been developed in the environmental sciences, where
spatial models are fitted to remote sensing data (Chabot et al., 2015; Janjić et al., 2018), and
these could be co-opted to this use case. In principle, the Bayesian statistical framework
used in parameter inference in plant disease epidemiology also provides a mechanism by
which these types of uncertainty can be propagated. However, despite some promising
successes in related fields (e.g., Bauer-Marschallinger et al., 2022), methods to do this
specifically for optical sensing data and plant disease will require more research.

### D) Particular challenges associated with emerging diseases

Reacting rapidly to invasion of a region hitherto unaffected by a plant pathogen is important
to give disease management the best possible chance of success (Epanchin-Niell, 2010;
Fraser, 2004). However, since spread data only become available as any outbreak unfolds (Thompson et al., 2018), pathogen biology and transmission become more precisely characterised the longer any epidemic has been spreading in the region of interest (Neri et al., 2014). This unavoidable tension between when models are most useful and when the data to drive them become available leads to challenges characteristic of emerging plant disease epidemics, affecting both optical sensing and epidemiological modelling.

A challenge is that reference data are almost always more limited for emerging than for established diseases. The probability a disease will truly be present if detected - the “positive predictive value”, PPV (Bours et al., 2021) - is likely to be low. Indeed, the PPV depends on the prevalence of the disease (*a priori* low for an emerging disease) and the sensitivity and specificity of the detection method. With sensitivity and specificity of 90%, the PPV is 0.083 for a prevalence of 1%, meaning that the disease is truly present in only 8.3% of detections. This value drops to 0.89% for a prevalence of 0.1%. Nearly perfect sensors with 99% sensitivity/specificity are necessary to reach PPV of 50% (at prevalence 1%) and 9% (at prevalence 0.1%). The levels of sensitivity and specificity of optical sensing in the field depend on many factors, but values higher than 90-95% are currently unlikely (Terentev et al., 2022).

Arguably the larger challenge for optical sensing of emerging pathogens is that spectral signatures are often not characterised. It would, of course, be plausible to use signatures from geographic regions where the pathogen of interest is well-established and well-characterised (Negrisoli et al., 2022; Gongora-Canul et al., 2020; Zhang et al., 2023). But this raises challenges around the robustness/transferability of the signatures from different geographic areas. A second approach could be to use proximal sensing and reference disease intensity data from controlled environment experiments. However, here the related challenge is the robustness/transferability between controlled environments and epidemics in the field (see Challenge Aiii).

An approach which simultaneously targets a range of potential invading pathogens while sidestepping the need to obtain disease-specific spectral signatures in a novel environment is “anomaly detection”. Spectral signatures associated with healthy plants are characterised and deviation from typical signatures then acts as a trigger to initiate ground scouting or other efforts (Kanaley et al., 2024). It may be possible to derive robust and species specific spectral signatures of plant health based on foliar functional plant traits (Reich et al., 1997; Wright et al., 2004). Some of these traits (e.g., leaf mass per area, chlorophylls) may reflect overall plant health, while others (e.g., lignins, carotenoids) hint at diseases. Robust estimation of many of these traits via optical sensing has been achieved (Singh et al., 2015; Wang et al., 2019, 2020; Zhang et al., 2021; Cherif et al., 2023). Measuring abnormal plant mortality (Wegmueller et al., 2024) and detecting abnormal changes in plant traits (Fekete and Cserep, 2021) via optical sensing combined with novelty detection classification techniques (AlSuwaidi et al., 2018) may provide valuable information about emerging diseases. These approaches may become especially effective in nursery and greenhouse production: a relatively small footprint and controlled growth conditions makes it easier to characterise and monitor spectral signatures of healthy plants. However, going from characterisation of functional plant traits to a robust assessment of plant health requires a nontrivial synthesis of existing knowledge/data and dedicated new datasets.

Parameterised mathematical models will also tend not to be available when a pathogen is emerging and spreading in a new region. Indeed, fully parameterised predictive models have often only become available after control has ceased to be a viable proposition (e.g., sudden oak death in California (Meentemeyer et al., 2011; Cunniffe et al., 2016)). Similarly to the trade-offs for optical sensing above, options for making models available before or during outbreaks tend to require either significant assumptions on pathogen spread (e.g., Hilker et al., 2017) or direct transfer of models originally parameterised for spread in other locations
(e.g., Ellis et al., 2025). Both options introduce uncertainties and potentially inaccuracies in spread predictions. Predictive models of emerging pathogens are therefore particularly challenging to develop (Cunniffe et al 2015; Cunniffe & Gilligan, 2020), and links with optical sensing must be alert to this. Where possible, a combination approach - use of predictive models along with anomaly detection, described above - may help to improve timely detection of emerging diseases.

Finally, ethical considerations, including privacy and data sharing concerns, can pose particular challenges for emerging diseases (Challenge Biv). Emerging diseases tend to require interdisciplinary collaboration between remote sensing specialists, epidemiologists, and plant pathologists, sometimes under significant time pressure, and this may not be easy. In many developing countries, limited infrastructure and resources, a lack of experts in relevant fields, or limited funding might reasonably be expected to lead to particularly extreme challenges in this regard.

We summarise the opportunities and challenges presented above in Figure 1.

## Recommendations to support integration of optical sensing and epidemiological modelling

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Summary of recommendations to support integration of optical sensing and epidemiological modelling.</figcaption></figure>

# Recommendations

## 1. Establish standards

There is a critical need to standardise methods for optical sensing in plant health monitoring, to acquire data comparable across sensors, sites and dates. This includes developing common protocols for data acquisition, processing, and interpretation; ideally this should be led by experts in these fields. Assuming they are widely adopted, consistent, reproducible and reliable practices will help minimise bias, improve accuracy, and enable comparability
across studies. This is critical for hyperspectral imaging due to the complexity of the sensors' operation, data acquisition and processing (Aasen et al., 2018). To convert hyperspectral imagery into surface reflectance (the proportion of incoming light reflected by a surface), it is essential to measure irradiance (the amount of incoming sunlight) at the time of image capture. This requires recording irradiance data simultaneously with the other imagery captured by measurement platforms. Radiometric calibration of the sensors and standard models (such as RTM; Verhoef and Bach, 2003) need to be optimised for local conditions to enable atmospheric correction of the imagery. For UAV platforms, Chakhvashvili et al. (2024) propose a structured approach to multi-sensor campaigns encompassing mission planning, calibration and spatial referencing and using additional sensors to assess ambient environmental conditions (e.g., weather stations, Internet of Things environmental sensors). A recent positive development is the publication of a European and Mediterranean Plant Protection Organization (EPPO) standard on "Adoption of digital technology for data generation for the efficacy evaluation of plant protection products" (Anonymous, 2024).

## 2. Develop, maintain and use open access databases

Open access, standardised databases including optical sensing data, epidemiological models, and reference data, would foster cross-disciplinary work (Sparks et al., 2023). However, data privacy and intellectual property implications would need attention (Kaur et al., 2022), as would long-term funding to maintain such a system. To achieve this, we can draw inspiration from genomics, where open access data repositories are well established (e.g., GenBank of the National Center for Biotechnology Information). Using openEO (https://dataspace.copernicus.eu/analyse/apis/openeo-api) in remote sensing of plant diseases would provide a standardised, scalable, and interoperable platform that simplifies access to diverse Earth observation datasets (Schramm et al., 2021).

## 3. Develop awareness by working with stakeholders

To address socio-economic constraints, governments and international organisations should improve data regulation, legislation and digital literacy (Kos and Kloppenburg, 2019). Our research communities need to work with social scientists and stakeholders to find ways to reconcile data availability with respecting data privacy and intellectual property (Everts et al., 2012; Kaur et al., 2022).

## 4. Routinely capture a range of conditions to improve generalisability & transferability

Many studies report disease measurement via optical sensing, but the outcomes may not be robust with respect to other biotic or abiotic stresses and may not be transferable to other host genotypes or geographic locations. To address these challenges, comprehensive ranges of conditions (related to host plant, pathogen and the environment) need to be captured in both reference and optical sensing measurements, which need to be georeferenced. Possible abiotic and biotic confounding factors also need to be assessed in the field.

## 5. Use crowdsourcing and gamification to improve annotation of data when possible

Annotated reference data for model training is a key limiting factor, and crowdsourcing may help to overcome this (Wazny, 2017). However, despite the emergence of various paid for platforms, e.g., Amazon's Mechanical Turk (Mason & Suri, 2012), and the possibility to use gamification to reduce costs (Khakpour & Colomo-Palacios, 2020), the necessary specialist knowledge required to annotate plant diseases might make this challenging (Bock et al., 2020). Yet, these efforts would be of great educational value and help to promote plant health to wider audiences.
## 6. Optimise optical sensing data collection both for use with models and by using models

Spatial and temporal scales and resolutions and trade-offs between them need to be considered when combining optical sensing and epidemiological modelling. Acquisition of optical sensing data can be optimised using epidemiological modelling, but model development needs to be informed by the characteristics of the available optical sensing data.

## 7. Identify signatures of plant health beyond the one host-one pathogen paradigm

Linking epidemiological models and optical sensing data is difficult because it is hard to identify a given disease, particularly given the range of biotic and abiotic conditions that must be handled (Challenges A). Focusing on anomaly detection is therefore very attractive, although it requires us to overcome the significant challenge of robustly assessing plant health from measured traits (while accounting for multiple pathogens).

## 8. Ensure uncertainties are captured and propagated through analyses

Uncertainty can be introduced at various stages in analytic pipelines, from uncertainty in measurements (e.g., due to cloud cover), to confusions caused by interactions with biotic and/or abiotic factors (Challenge Aii), to errors or imprecision in machine learning methods for processing data (Qin and Liu, 2022), to uncertainties in model parameters as fitted to data (Minter and Retkute, 2019), to sampling effects when using stochastic models predictively (Cunniffe and Gilligan, 2020). Sorely needed are methods to capture and propagate these uncertainties forward, building on promising methods from related fields (Charbot et al., 2015).

## 9. Establish multidisciplinary collaborations

We need to foster multidisciplinary and interdisciplinary collaborations, bringing together optical sensing experts, computer scientists, plant pathologists, plant physiologists, crop modellers and epidemiological modellers (Camino et al., 2021). Encouragingly, a growing body of work in phytopathometry (Gongora-Canul et al., 2020; Kanaley et al., 2024; Lee et al., 2021; Lee et al., 2025; Oh et al., 2021; Zhang et al., 2023) exemplifies this, and shows how integrated methodologies can enhance the reliability and scalability of plant disease detection, quantification, and assessment under field conditions. In going further and linking optical sensing with epidemiological modelling, we should not reinvent the wheel but instead draw inspiration from disciplines such as environmental sciences (Liu, 2015; Weng, 2009) and meteorology (Bevis et al., 1992; Mittaz et al., 2019), which have long coupled optical data with mathematical modelling. We can also reflect on other uses of new sources of data in epidemiological modelling. Notable examples include phylogenetic data (Pybus and Rambaut, 2009; Gougherty and Davies, 2021), and human mobility data from mobile phones and social media (Grantz et al., 2020; Kostandova et al., 2024).

## 10. Teach basic sciences and modern data analysis in plant pathology training

A major obstacle to integrating optical sensing and epidemiological modelling is the inconsistent and often insufficient training in basic sciences and modern data analysis at the bachelors and masters levels in agricultural and biological sciences. While addressing this requires systemic changes and broader discussions across the academic community, there are practical steps we can take to train the next generation of plant health researchers. These include: (i) designing and teaching courses on digital plant health, incorporating necessary elements of basic sciences (mathematics, physics, chemistry, and biology), programming, data sciences and mathematical modelling; (ii) organising summer schools on interdisciplinary approaches to plant health; (iii) organising informal study groups and other
communities that bring together students and researchers from different disciplines, perhaps leveraging internet technologies to do so. Ensuring accessibility to these opportunities — particularly for researchers from the Global South and underrepresented communities — should be a key priority. In doing this, we can draw inspiration from similar discussions in related multidisciplinary fields such as bioinformatics (Mulder et al., 2018) and big data/artificial intelligence (Luan et al., 2020).

## 11. Promote opportunities to funding agencies, governments, plant protection organisations and technology companies

Interdisciplinary and transdisciplinary research in digital plant health must be supported more strongly by funding agencies. Traditional three-year funding periods are often too short to perform the necessary field trials or observational studies, collect and analyse data, and publish the outcomes. More comprehensive support, longer term funding and interdisciplinary projects are needed to collect these datasets, transform them into meaningful interpretations and publish them open access in accordance with the findable, accessible, interoperable, and reusable (FAIR) data principles (Kumar et al. 2024). This approach is data intensive, and therefore we need to establish the necessary infrastructure to develop sophisticated artificial intelligence models (e.g., self-supervised learning or foundation models) in cooperation with machine learning experts. Further, we need to collaborate with plant protection companies and technology companies to make the applications rapidly accessible and to foster their adoption by growers. A particular challenge is to communicate with political decision-makers and convince them of the many possibilities and necessary steps, as well as the commensurate need for investment.

We summarise the recommendations presented above in Figure 2.

## Acknowledgements

This paper was initiated during the satellite meeting "How to combine remote sensing with epidemiological modelling to improve plant disease management", organised as part of the 12th International Congress for Plant Pathology in Lyon, France in August 2023. The satellite meeting would not have been possible without funding provided by the BSPP (British Society for Plant Pathology), SFP (Société Française de Phytopathologie) and INRAE (Plant Health & Environment division, Mathematics & Digital Technologies division and ModStatSAP, the research network in Modelling and Statistics for Animal and Crop Health). Publication of the work was supported by the Agricultural Biosecurity program, project award no. 2024-67013-42399, from the U.S. Department of Agriculture's National Institute of Food and Agriculture.

A. M. gratefully acknowledges funding by the Royal Society Research Grant RG\R1\251181 and the University of Reading (UK). A.-K. M. and A. B. gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2070-390732324 – PhenoRob. F. F. gratefully acknowledges the IMPACT Project (agreement 23-1315) of the APR 'Extended Epidemiological Surveillance (an action of Ecophyto II+ plan) supported by the French Office for Biodiversity. S. S. gratefully acknowledges the support of the BEYOND project (Grant ANR-20-PCPA-0002) and the SEPIM project (FranceAgriMer Grant 3890396). J. R. and N. J. C. gratefully acknowledge support from Girton College, University of Cambridge. S. F. and E. M. gratefully acknowledge the funding to the Resilient Forests Programme from the New Zealand Ministry of Business, Innovation & Employment through Strategic Science Investment Funding to Scion and the Forest Growers Levy Trust. M. L. acknowledges the support of the PI@ntAgroEco (Grant ANR-22-PEAE-0009) and AgroStat (Grant ANR-23-EXMA-0002) projects. C. D. C. gratefully acknowledges support by the Agricultural
Biosecurity project, through award number 2023-67013-39300, from the U.S. Department of Agriculture's National Institute of Food and Agriculture.

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