Leveraging machine learning tools and algorithms for analysis of fruit fly morphometrics

Analysis of landmark-based morphometric measurements taken on body parts of insects have been a useful taxonomic approach alongside DNA barcoding in insect identification. Statistical analysis of morphometrics have largely been dominated by traditional methods and approaches such as principal component analysis (PCA), canonical variate analysis (CVA) and discriminant analysis (DA). However, advancement in computing power creates a paradigm shift to apply modern tools such as machine learning. Herein, we assess the predictive performance of four machine learning classifiers; K-nearest neighbor (KNN), random forest (RF), support vector machine (the linear, polynomial and radial kernel SVMs) and artificial neural network (ANNs) on fruit fly morphometrics that were previously analysed using PCA and CVA. KNN and RF performed poorly with overall model accuracy lower than "no-information rate" (NIR) (p value > 0.1). The SVM models had a predictive accuracy of > 95%, significantly higher than NIR (p < 0.001), Kappa > 0.78 and area under curve (AUC) of the receiver operating characteristics was > 0.91; while ANN model had a predictive accuracy of 96%, significantly higher than NIR, Kappa of 0.83 and AUC was 0.98. Wing veins 2, 3, 8, 10, 14 and tibia length were of higher importance than other variables based on both SVM and ANN models. We conclude that SVM and ANN models could be used to discriminate fruit fly species based on wing vein and tibia length measurements or any other morphologically similar pest taxa. These algorithms could be used as candidates for developing an integrated and smart application software for insect discrimination and identification. Variable importance analysis results in this study would be useful for future studies for deciding what must be measured.