Machine learning model to predict endophytic colonisation of rice cultivar plant tissues by Beauveria bassiana isolates and their potential as bio-control agents against rice stem borer using existing knowledge

Abstract

Background: Finding well-known Beauveria bassiana isolates that could preserve rice crops from Sesamia calamistis (stem borer) is problematic. Another difficult task is the development of precise inoculation methods, which have been employed for their establishment as endophytes in cereal crops. This study proposed machine learning models to predict the best entomopathogenic fungi, Beauveria bassiana that could directly protect rice crops against Sesamia calamistis. Methods: Data driven machine learning decisions were implemented and assessed from 60 experimental runs with nine different feature/input variables and three target/output variables following foliar spray and seed treatment inoculation method. The feature variables consisted of rice plant tissue, such as Nerica-L19, Nerica1, Nerica8, the time, and the five promising isolates Beauveria bassiana (Bb3, Bb4, Bb10, Bb21, Bb35). The target variable consisted of the number of colonised roots, stems and leaves, expressed as a percentage depending on the degree of protection after each inoculation. A data driven decision by the extreme gradient boosting regression algorithm was used to proficiently abstract the situation where there is no direct relationship between features and target variables. Results: The foliar spray inoculation method exhibited high coefficient of determination (R2) of 0.99, 0.98 and 0.94 depending on the number of colonised stems, roots and leaves, respectively, while the seed treatment approach exhibited the coefficient of determination (R2) of 0.91, 0.87 and 0.75, respectively. Conclusions: These results demonstrated that the Extreme Gradient Boosting algorithm effectively abstracted the nonlinear relationship between the attribute variables that were taken into consideration and predicted Beauveria bassiana as a bio-pesticide for rice and perhaps other cereal stem borers. Thus, this XGBoost regression model could be used to navigate the optimization domain and reduce the development time of the biocontrol process.