Unsupervised detection of biotic and abiotic crop stress using Sentinel-2 time series and Isolation Forest

Detection of biotic and abiotic stress at the field level is an important crop monitoring task with varied applications, including the delineation of management zones and targeted management interventions. Satellite remote sensing provides extensive spatial and temporal coverage for this purpose; however, automated stress detection is constrained by a lack of field-level ground-truth data required to train supervised models. This study develops and evaluates an unsupervised anomaly-detection workflow for identifying biotic and abiotic crop stress using openly available Sentinel-2 satellite imagery, without relying on ground-truth labels. The study develops an Isolation Forest–based method incorporating within-season time-series data that include spectral bands and vegetation indices. Unlike traditional statistical anomaly-detection methods, this model-based technique accommodates multivariate inputs and does not require the assumption of a normal data distribution. Multiple feature configurations were assessed, including visible, red-edge, near-infrared, and shortwave infrared bands, their combinations, and selected vegetation indices. Anomaly scores were computed across multiple image acquisition dates, and only regions consistently identified as anomalous over time were retained as persistent stress signals. The framework was evaluated across three different stress scenarios; frost damage, Septoria disease incidence, and nitrogen deficiency. Results show that the proposed approach successfully detected stress patterns across all sites, achieving accuracies of up to 83%. In addition, the experiments identified key spectral features that were particularly informative for detecting each specific type of stress. This workflow offers a scalable and operationally feasible option for crop stress detection in agricultural systems where ground-truth data are limited.