Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds
Table 2
Predictive performance of different Machine-Learning algorithms on acoustic recognition of bee species 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 (MacPred), Macro-Recall (MacRec) and Macro-F1 (MacF1) and compared with three baselines scenarios: (1) Majority class: assigning all the classes to the majority class; (2) Fundamental frequency: bees recognition based solely on the average frequency of the sonication, as performed by [43]; (3) Fundamental frequency (SVM): bees 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 F1-score 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).