Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery
Vegetative compatibility groups (VCGs) in fungi like <i>Verticillium dahliae</i> are important for understanding genetic diversity and for informed plant disease management. This study utilized hyperspectral imagery (HSI) and machine learning to differentiate the VCGs of <i>V. dahl...
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2025-04-01
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| author | Sudha GC Upadhaya Chongyuan Zhang Sindhuja Sankaran Timothy Paulitz David Wheeler |
| author_facet | Sudha GC Upadhaya Chongyuan Zhang Sindhuja Sankaran Timothy Paulitz David Wheeler |
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| description | Vegetative compatibility groups (VCGs) in fungi like <i>Verticillium dahliae</i> are important for understanding genetic diversity and for informed plant disease management. This study utilized hyperspectral imagery (HSI) and machine learning to differentiate the VCGs of <i>V. dahliae</i>. A total of 194 isolates from VCGs 2B and 4A and 4B were cultured and imaged across the 533–1719 nm spectral range, and the spectral, textural, and morphological features were extracted. The study documented the spectral profiles of <i>V. dahliae</i>’s isolates and identified specific spectral features that can effectively differentiate among the VCGs. Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. The results showed that combining spectral, textural, and morphological data provided the highest classification accuracy. The ANN model achieved a 79.4% accuracy overall, with an 87% accuracy for VCG 2B and 88% for VCG 4A, but it had consistently low accuracies for VCG 4B. Although this work utilized only three of the nearly eight known VCGs, the findings underscore the potential of the HSI for fungal group classification. The study also highlights the need for future work to include a wider range of VCGs from multiple regions, larger sample sizes, and careful selection of feature sets to enhance model performance and generalizability. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-a80e14e59c69477a8343e76a65bb36e72025-06-25T13:24:21ZengMDPI AGApplied Microbiology2673-80072025-04-01524110.3390/applmicrobiol5020041Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral ImagerySudha GC Upadhaya0Chongyuan Zhang1Sindhuja Sankaran2Timothy Paulitz3David Wheeler4Department of Plant Pathology, Washington State University, Pullman, WA 99164, USADepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN 47907, USADepartment of Biological Systems Engineering, Washington State University, Pullman, WA 99164, USAUnited States Department of Agriculture, Agricultural Research Service, Pullman, WA 99164, USAUdemy Inc., San Francisco, CA 94107, USAVegetative compatibility groups (VCGs) in fungi like <i>Verticillium dahliae</i> are important for understanding genetic diversity and for informed plant disease management. This study utilized hyperspectral imagery (HSI) and machine learning to differentiate the VCGs of <i>V. dahliae</i>. A total of 194 isolates from VCGs 2B and 4A and 4B were cultured and imaged across the 533–1719 nm spectral range, and the spectral, textural, and morphological features were extracted. The study documented the spectral profiles of <i>V. dahliae</i>’s isolates and identified specific spectral features that can effectively differentiate among the VCGs. Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. The results showed that combining spectral, textural, and morphological data provided the highest classification accuracy. The ANN model achieved a 79.4% accuracy overall, with an 87% accuracy for VCG 2B and 88% for VCG 4A, but it had consistently low accuracies for VCG 4B. Although this work utilized only three of the nearly eight known VCGs, the findings underscore the potential of the HSI for fungal group classification. The study also highlights the need for future work to include a wider range of VCGs from multiple regions, larger sample sizes, and careful selection of feature sets to enhance model performance and generalizability.https://www.mdpi.com/2673-8007/5/2/41verticillium wilt<i>V. dahliae</i>fungivegetative compatibility groupsfungal classificationhyperspectral imaging |
| spellingShingle | Sudha GC Upadhaya Chongyuan Zhang Sindhuja Sankaran Timothy Paulitz David Wheeler Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery Applied Microbiology verticillium wilt <i>V. dahliae</i> fungi vegetative compatibility groups fungal classification hyperspectral imaging |
| title | Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery |
| title_full | Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery |
| title_fullStr | Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery |
| title_full_unstemmed | Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery |
| title_short | Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery |
| title_sort | classification of i verticillium dahliae i vegetative compatibility groups vcgs with machine learning and hyperspectral imagery |
| topic | verticillium wilt <i>V. dahliae</i> fungi vegetative compatibility groups fungal classification hyperspectral imaging |
| url | https://www.mdpi.com/2673-8007/5/2/41 |
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