Taxonomy-agnostic hyperspectral–morphological phenotyping of fungal pathogen chemical-stress responses using machine learning

• Taxonomy-agnostic workflow integrates hyperspectral imaging and morphology. • Machine learning predicts crop-source labels with 86.7% accuracy in mixed lineages. • System exhibits robustness, maintaining >86% accuracy even after feature ablation. • Non-linear ML captures structure missed by linear stats (1.7% variance). • Enables rapid, sensor-driven antifungal screening without prior DNA sequencing. Can standardized chemical stress reveal a reproducible, taxonomy-agnostic stress-response fingerprint that is predictive of sample-source labels (crop-of-isolation) in fungal isolates? Six coffee‑associated Colletotrichum isolates were profiled using four phenolic‑branched compounds and compared them with a previously characterized cacao panel. Quantitative morphology, hyperspectral imaging (HSI), and supervised machine learning (ML) yielded panel-specific fingerprints under uniform, isotropic in vitro conditions. Circularity, a measure of edge symmetry, was the most informative morphological feature, and ML classified the crop-of-isolation label (coffee vs cacao, in this panel) with 86.7% accuracy in within-panel cross-validation. HSI detected dose-dependent spectral shifts in a targeted subset of isolates and compounds, including changes near 1930 nm in the short-wave infrared, a moisture-sensitive region that warrants robustness checks (e.g., band masking or preprocessing sensitivity) prior to biochemical attribution. Multi‑locus phylogeny showed the coffee isolates are polyphyletic, so the predictive signal should be interpreted conservatively as a taxonomy-agnostic phenotype fingerprint associated with crop background in this mixed-lineage panel, acknowledging that crop labels are partially confounded with phylogenetic structure. We propose a “chemical priors” framework as a working hypothesis, in which long-term environmental exposure may imprint stress-response pathways that become legible under simple, standardized probes. This integrative workflow supports scalable screening of eco-friendly antifungals and sensor-driven decision support for high-throughput phenotype-based screening workflows.