Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics

The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. This approach was validated using a previously published dataset. A subgroup of the identified “anomalies” was clearly linked to stress-induced reductions in photosynthesis. Furthermore, the percentage of these “anomalies” showed a meaningful correlation with both the progression and severity of stress. The results highlight the still largely unexploited potential of Machine Learning in OJIP analysis.