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...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2673-7140/4/4/51 |
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| author | Nam Trung Tran |
| author_facet | Nam Trung Tran |
| author_sort | Nam Trung Tran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4e003a629e4a444b96c854b12529f05f |
| institution | DOAJ |
| issn | 2673-7140 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stresses |
| spelling | doaj-art-4e003a629e4a444b96c854b12529f05f2025-08-20T02:51:07ZengMDPI AGStresses2673-71402024-11-014477378610.3390/stresses4040051Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence KineticsNam Trung Tran0Applied Plant Sciences, Department of Biology, Technical University Darmstadt, 64287 Darmstadt, GermanyThe 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.https://www.mdpi.com/2673-7140/4/4/51chlorophyll fluorescenceOJIPstressmachine learningone-class classificationanomaly detection |
| spellingShingle | Nam Trung Tran Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics Stresses chlorophyll fluorescence OJIP stress machine learning one-class classification anomaly detection |
| title | Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics |
| title_full | Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics |
| title_fullStr | Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics |
| title_full_unstemmed | Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics |
| title_short | Anomaly Detection Utilizing One-Class Classification—A Machine Learning Approach for the Analysis of Plant Fast Fluorescence Kinetics |
| title_sort | anomaly detection utilizing one class classification a machine learning approach for the analysis of plant fast fluorescence kinetics |
| topic | chlorophyll fluorescence OJIP stress machine learning one-class classification anomaly detection |
| url | https://www.mdpi.com/2673-7140/4/4/51 |
| work_keys_str_mv | AT namtrungtran anomalydetectionutilizingoneclassclassificationamachinelearningapproachfortheanalysisofplantfastfluorescencekinetics |