Classification of Verticillium dahliae Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery

Vegetative compatibility groups (VCGs) in fungi like Verticillium dahliae 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 V. dahliae. 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 V. dahliae’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.