Predictive performance of different Machine-Learning algorithms on acoustic recognition of bee genera based on the type of buzzing-sound (flight, sonication, and flight+sonication) during visits to tomato flowers.

The performance of the ML algorithms was measured by Accuracy (Acc), Macro-Precision (MacPrec), Macro-Recall (MacRec) and Macro-F1 (MacF1) and compared with three baseline scenarios: (1) Majority class: assigning all the classes to the majority class; (2) Fundamental frequency: bee recognition based solely on the average frequency of the sonication, as performed by [43]; (3) Fundamental frequency (SVM): bee recognition based fundamental frequency and using the SVM algorithm, classifier with the best performance (based on the MacF1 score). Bold numbers represent the best results per evaluation metric within buzz-sound; Different upper side letters denote significant differences in the MacF1 scores among the algorithms of the same buzzing-behavioral (p ≤ 0.05, T-test); (**) denotes that the performance of the algorithm is higher than the baselines (based on the MacF1 measure; p ≤ 0.05, T-test).