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<table><caption>Table 1. List of acronyms.</caption><thead><tr><td>Acronym</td><td>Full Form</td></tr></thead><tbody><tr><td>UV</td><td>Ultraviolet</td></tr><tr><td>IR</td><td>Infrared</td></tr><tr><td>UAVs</td><td>Uncrewed Aerial Vehicles</td></tr><tr><td>RGB</td><td>Red-green-blue</td></tr><tr><td>CIR</td><td>Colour-Infrared</td></tr><tr><td>HSI</td><td>Hyperspectral Imaging</td></tr><tr><td>NIR</td><td>Near-Infrared</td></tr><tr><td>SWIR</td><td>Shortwave Infrared</td></tr><tr><td>TIR</td><td>Thermal Infrared</td></tr><tr><td>LiDAR</td><td>Light Detection and Ranging</td></tr><tr><td>VNIR</td><td>Visible and Near-Infrared</td></tr><tr><td>SWIR</td><td>Shortwave Infrared</td></tr><tr><td>MSI</td><td>Multispectral Imaging</td></tr><tr><td>BRDF</td><td>Bidirectional Reflectance Distribution Function</td></tr><tr><td>RTM</td><td>Radiative Transfer Models</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>OQDS</td><td>Olive Quick Decline Syndrome</td></tr><tr><td>CLS</td><td>Cercospora Leaf Spot</td></tr><tr><td>CNN</td><td>Convolutional Neural Networks</td></tr><tr><td>PLSR</td><td>Partial Least Squares Regression</td></tr><tr><td>SSL</td><td>Self-Supervised Learning</td></tr><tr><td>DA</td><td>Data Assimilation</td></tr><tr><td>PPV</td><td>Positive Predictive Value</td></tr></tbody></table>
<table><caption>Table 2. Plant traits and features that, once estimated via optical sensing, could aid in epidemiological modelling, and vice versa.</caption><thead><tr><td>Plant trait/feature (estimated via optical sensing)</td><td>How optical sensing aids epidemiological modelling</td><td>How epidemiological modelling aids optical sensing</td></tr></thead><tbody><tr><td>Disease onset, incidence, and severity</td><td>Improves model parameterisation and validation with objective, standardised, high-resolution data</td><td>Improves classification by incorporating risk estimates derived from models</td></tr><tr><td>Spatio-temporal patterns of infection</td><td>Enhances understanding of disease dynamics and spread (i.e., data for model fitting)</td><td>Informs contextual interpretation based on expected and/or modelled spatial clustering</td></tr><tr><td>Pathogen dispersal gradients</td><td>Improves estimation of dispersal kernels by providing additional data</td><td>Helps validate sensing-derived assessment of pathogen dispersal with models accounting for underpinning mechanism of pathogen spread</td></tr><tr><td>Real-time infection status or anomalies</td><td>Triggers surveillance or action based on spectral anomalies (underpinned by tests in models)</td><td>Optimises sensor deployment (e.g., UAV routes) to maximise information content in data</td></tr><tr><td>Host plant identity (species, cultivar)</td><td>Increases biological realism in host maps used in models</td><td>Informs whether there is a need for species-level resolution in host plant sensing</td></tr><tr><td>Host density and spatial distribution</td><td>Enables dynamic modelling of disease risk based on real host distributions in space</td><td>Focuses sensing efforts where areas of higher host density are expected to be most epidemiologically relevant</td></tr><tr><td>Host phenology and growth stage</td><td>Allows time-sensitive modelling of host susceptibility and epidemic timing</td><td>Highlights critical phenological windows (and spatial locations) for data collection using optical-sensing</td></tr><tr><td>Environmental conditions (e.g., topography, water availability)</td><td>Adds environmental realism to models, potentially improving predictive accuracy</td><td>Identifies which environmental variables are most relevant to measure (i.e., have the largest effects on disease risk)</td></tr><tr><td>Presence of inoculum reservoirs or alternative hosts</td><td>Informs model structure by allowing models to account for hidden reservoirs</td><td>Suggests where to search for reservoirs based on persistence/spillover inferred with models</td></tr><tr><td>Confirmation of control implementation (e.g., host removal)</td><td>Improves tracking and evaluation of management interventions, then informing models</td><td>Targets verification efforts on areas of predicted but uncertain control (e.g., due to lack of stakeholder compliance)</td></tr></tbody></table>
# Supplementary Material S1

## Glossary

**Abiotic stress:** Non-living environmental factors such as drought, temperature, and salinity that can cause stress to plants, and which can be distinguished from biotic stress caused by living organisms.

**Anomaly detection:** In the context of optical sensing, a method used to identify spectral signatures associated with healthy plants, where deviations from typical signatures can indicate the presence of disease.

**Bayesian analysis:** A statistical method that incorporates prior knowledge or beliefs, along with new evidence and a probabilistic model of a process, to update a probability or probability distribution.

**Bidirectional reflectance:** The reflection of light from a surface that can vary depending on the angle of illumination and observation.

**Biotic stress:** Stress caused by living organisms such as pathogens, pests or weeds.

**Chlorophyll fluorescence:** A technique that measures the re-emission of absorbed light by chlorophyll molecules in plants, used as an indicator of a pathogen's effect on photosynthesis.

**Compartmental models:** A modelling approach in epidemiology that divides a host population into distinct groups (compartments) based on disease status, such as susceptible, exposed, infected, and recovered (SEIR).

**Data assimilation:** The process of integrating observational data with model predictions to improve the accuracy of forecasts.

**Data fusion:** The process of integrating information from various sources to achieve results that are unattainable from a single source alone, such as combining imagery and weather data for disease detection and quantification.

**Disease incidence:** A metric indicating the intensity of disease within a plant population, usually represented as the proportion of diseased specimens to the total number of specimens evaluated, regardless of the assessment method.

**Disease severity:** A metric indicating the degree to which a plant, plant part, or defined area of land is affected by a disease, often measured by metrics such as the percentage or proportion of area diseased (0-100%), the number (N) and density or size of lesions, or other symptom descriptions using ordinal scales.

**Dispersal kernel:** A description of the probability of dispersal events as a function of distance, important for modelling the spread of plant diseases.

**Epidemiology:** The study of changes in disease intensity in a plant host population over time and space.

**Epidemiological models:** Mathematical representations of how diseases spread within populations.
**Feature extraction:** The process of identifying and isolating relevant information or patterns from sensor data, whether remote or proximal sensing, for further analysis or modelling.

**Foliar scale:** Pertaining to leaves or the individual leaf level.

**Foundation models:** Large-scale machine learning models trained on diverse data to capture general patterns, allowing them to be fine-tuned for various tasks without retraining from scratch. Unlike self-supervised models, they are built for broad adaptability across tasks.

**Functional plant traits:** Characteristics of plants (e.g., leaf mass per area, chlorophylls, lignins, carotenoids) that reflect overall plant health and can hint at diseases.

**Generalisability:** Property or ability of a model or its predictions/outputs to be applicable across different host genotypes, locations, and time periods beyond the specific setting where it was developed or validated.

**Hierarchical Bayesian model (HBM):** A framework used for model inference where the full model is made up of a series of sub-models organised in different layers. The HBM links the sub-models together, correctly propagating uncertainties in each sub-model from one level to the next and estimates posterior distributions using the Bayesian framework.

**Hyperspectral sensing:** Technology that captures and analyses a wide spectrum of light across many contiguous spectral bands.

**Hyperspectral imaging (HSI):** Remote sensing technique that captures and processes information across a wide range of the electromagnetic spectrum with a high spectral resolution, allowing detailed analysis of specific spectral bands.

**Integrated pest management (IPM):** A decision-making process for managing pests (often understood here to include pathogens) using a combination of management interventions in an effective, economical, and environmentally sound way, often utilising data from various sources.

**LiDAR (light detection and ranging):** A remote sensing method that uses light in the form of a pulsed laser to measure distances.

**Machine learning (ML):** A subset of artificial intelligence that involves training algorithms to recognise patterns in data.

**Machine learning classification (ML classification):** The process of using algorithms to automatically classify data into predefined categories.

**Mechanistic-statistical model:** A modelling approach that combines (i) a mathematical (deterministic or stochastic) model describing the main mechanisms governing the dynamics of the system of interest (e.g., an SEIR model) with (ii) a statistical model connecting the state variables of the mechanistic model with a probabilistic model describing the observation process. Inference of this class of model is most easily achieved using hierarchical Bayesian models.

**Monocyclic epidemic:** An epidemic caused by a pathogen that completes only one infection cycle per host cycle.
**Multimodal sensing:** The integration of multiple sensing methods to improve detection and characterisation of plant health and diseases.

**Multispectral imaging:** Remote sensing that captures data in specific wavelength bands of the electromagnetic spectrum.

**Nadir view:** A view directly downward from a satellite to the surface of the Earth.

**Novelty detection classification techniques:** Methods used in remote sensing to identify unusual or emerging diseases by detecting abnormal changes in plant traits.

**Overfitting:** A situation in which the parameters of a model become too closely aligned to the training data, resulting in poor performance on new, unseen data.

**Pathosystem:** The complex interactions between a host, a pathogen, and the environment.

**Plant canopy:** A multi-layered assembly of leaves, branches, and stems in areas like forests or agricultural fields, crucial for ecological functions. Its structure and microclimate can affect pathogen development and spread.

**Polycyclic epidemic:** An epidemic caused by a pathogen that completes multiple infection cycles per host cycle.

**Proximal sensing:** Measurement techniques conducted close to the plant, such as using handheld devices or sensors placed close to or in direct contact with crops to gather detailed data on plant health and disease characteristics.

**Radiative transfer approaches:** Methods used to model the transfer of radiation through a medium, such as the atmosphere or a plant canopy, accounting for absorption, scattering, and emission processes.

**Radiative transfer modelling (RTM):** A framework for simulating the propagation of electromagnetic radiation through a medium.

**Radiometric calibration:** The process of adjusting a sensor to ensure its output accurately reflects true radiance, typically done before data collection. This involves comparing sensor measurements against known reference standards to maintain measurement accuracy over time.

**Radiometric correction:** Adjustments made to image data acquired via optical sensing to correct for sensor errors and environmental factors, such as lighting and atmospheric conditions, ensuring the data accurately reflect the true surface radiance.

**Remote Sensing (RS):** The use of satellite or aerial imagery to monitor and assess conditions from a distance.

**Reference data:** A set of data used as a standard or benchmark to calibrate and validate data gathered from remote or proximal sensing technologies ("ground truth"), or for the development and training of algorithms or models (annotated or labelled).

**Robustness:** Ability of a model to maintain accurate predictions despite inconsistencies in the input data, including changes in data quality, environmental variability, and the presence of noise or uncertainty.
**Self-supervised learning (SSL)**: A type of machine learning where the model learns to generate labels from the input data itself, often used when labelled data is scarce.

**Shortwave infrared (SWIR)**: A specific range of wavelengths in the infrared spectrum used in remote sensing.

**Simulation models**: Models that use computer simulations to predict the behaviour of complex systems over time.

**Spatio-temporal dynamics**: The study of how patterns change over space and time.

**Species-specific spectral signature**: Unique spectral characteristics that can be used to identify and monitor the health of specific plant species.

**Spectral libraries**: Collections of spectral signatures used for comparison and identification purposes.

**Spectral reflectance**: The proportion of light that a surface reflects at different wavelengths, used in remote sensing to detect plant health and disease.

**Spectral responses**: The specific reactions or changes in spectral signatures due to different conditions, such as disease or stress.

**Spectral signature**: The specific pattern of reflectance intensities across the electromagnetic spectrum that is unique to a particular disease or stage of disease development.

**Spectral unmixing**: A technique used to decompose pixel-level reflectance into its constituent components, particularly in mixed pixels that contain multiple materials or land cover types.

**Spectral vegetation index**: A numerical indicator calculated from the spectral reflectance values of a surface at specific wavelengths in the electromagnetic spectrum. These indices are designed to quantify various vegetation characteristics, such as density, health, and photosynthetic activity. The most widely used and well-known spectral vegetation index is the Normalised Difference Vegetation Index (NDVI).

**Spectral resolution**: The ability of a sensor to distinguish between different wavelengths of light. Higher spectral resolution allows for finer differentiation of materials based on their spectral characteristics.

**Spatial resolution**: The detail with which a map depicts the location and shape of physical features, i.e., the smallest object that can be resolved by a remote sensing system.

**State variables**: Variables representing the current state of a system of interest at a specific time and location (e.g., the number of plants in different compartments in epidemiological models). In hierarchical Bayesian models, state variables are often considered as latent or hidden (i.e., not directly observable) components of the system of interest, with a dedicated part of these models connecting these latent state variables to the observed variables which are considered indirect manifestations of the underlying processes of interest.

**Temporal resolution**: The frequency at which data is collected.
**Thermal infrared imaging (TIR):** Detection of infrared radiation emitted by objects, enabling the assessment of plant stress or disease based on temperature variations.

**Transfer learning:** A machine learning technique where a model developed for one task is reused as the starting point for another task, common in using pre-trained models for specific agricultural or disease detection tasks.

**Uncrewed Aerial Vehicle (UAV):** An aircraft that operates without a pilot on board and which allows the attachment and integration of sensors for monitoring plant health and detecting diseases.

**Validation dataset:** In the context of remote sensing and epidemiological modelling, a validation dataset refers to a set of real-world, ground-truth data used to assess and confirm the accuracy of models or remote sensing outputs.

**Vector-borne pathogen:** A disease-causing organism (often viral or bacterial) transmitted by a different vector organism, very often an insect.