Monitoring wheat leaf rust severity using machine learning techniques

Wheat leaf rust, caused by Puccinia triticina Eriks., is recognized as one of the most destructive diseases affecting wheat worldwide, including Iran, resulting in substantial losses in grain yield and quality. This research focused on evaluating the pathogenic factors of nine leaf rust isolates collected from four different climates in Iran, using various differential genotypes. The assessment of leaf rust infection types was conducted on 49 durum and bread wheat genotypes, including susceptible control genotypes and 55 differential genotypes at the seedling stages. The results revealed a significant difference among wheat genotypes in their response to all isolates (p ≤ 0.01). Notably, certain genotypes, such as the Italian landrace (P.S. No4), Shabrang, Chamran2, Mehregan, Shosh, and Gonbad, exhibited resistance to all isolates at the seedling stage, indicating the presence of seedling resistance genes. Additionally, we determined the virulence/avirulence patterns for various resistance genes in the differential genotypes by assessing their responses to different isolates and recording the infection types. The findings indicated that all isolates were virulent on the lines carrying the Lr34 and Lr37 genes, whereas none of the isolates displayed a virulence on the lines carrying the Lr19 gene. This research provides valuable insights into the resistance patterns of wheat genotypes against leaf rust isolates in different climates in Iran, contributing to our understanding of the genetic basis of resistance and aiding in the development of effective strategies for disease management in wheat cultivation. The XGBoost (extreme gradient boosting) algorithm generated the most accurate predictions for the variables thousand grain weight and grain yield, while the MARS (multivariate adaptive regression spline) algorithm generated the most accurate predictions for the variables spike weight, number of grains per spike, and grain weight per spike. For each of these variables, GP (Gaussian process), MARS, and XGBoost achieved the lowest RMSE (root mean square error) values, indicating minimal prediction errors, and the highest R² values, signifying a strong correlation between the predicted and observed data. These prediction performances highlighted the robustness and accuracy of the GP, MARS and XGBoost algorithms in modeling wheat disease severity and its effects on yield outcomes.