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Statistical and Machine Learning-Based FHB Detection in Durum Wheat

Pathogens are the major causes of wheat crop yield losses, including the fungus Fusarium graminearum, an agent of Fusarium Head Blight (FHB). A better understanding of the relationship between plant morphological and biochemical traits and resistance to FHB can be effective in implementing a successful breeding program. This study investigated the relationship between FHB resistance as well as the morphological and biochemical traits in 20 durum wheat lines. Both morphological and biochemical traits were investigated using statistical tools. Therefore, analyses of variance, mean, as well as the correlation between the traits were considered. In addition, for the morphological traits, cluster analyses were performed to identify similar genotypes in control and infected conditions. Furthermore, machine learning (ML) classification techniques, including Support Vector Machine (SVM), were proposed to detect the infected plants using morphological traits. The results show a great promise for the application of data-driven ML-based methods in plant breeding and disease detection.