Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation.

Introduction: Bees capable of performing floral sonication (or buzz-pollination) are among the most effective pollinators of blueberries. However, the quality of pollination provided varies greatly among species visiting the flowers. Consequently, the correct identification of flower visitors becomes indispensable to distinguishing the most efficient pollinators of blueberry. However, taxonomic identification normally depends on microscopic characteristics and the active participation of experts in the decision-making process. Moreover, the many species of bees (20,507 worldwide) and other insects are a challenge for a decreasing number of insect taxonomists. To overcome the limitations of traditional taxonomy, automatic classification systems of insects based on Machine-Learning (ML) have been raised for detecting and distinguishing a wide variety of bioacoustic signals, including bee buzzing sounds. Despite that, classical ML algorithms fed by spectrogram-type data only reached marginal performance for bee ID recognition. On the other hand, emerging systems from Deep Learning (DL), especially Convolutional Neural Networks (CNNs), have provided a substantial boost to classification performance in other audio domains, but have yet to be tested for acoustic bee species recognition tasks. Therefore, we aimed to automatically identify blueberry pollinating bee species based on characteristics of their buzzing sounds using DL algorithms. Methods: We designed CNN models combined with Log Mel-Spectrogram representations and strong data augmentation and compared their performance at recognizing blueberry pollinating bee species with the current state-of-the-art models for automatic recognition of bee species. Results and Discussion: We found that CNN models performed better at assigning bee buzzing sounds to their respective taxa than expected by chance. However, CNN models were highly dependent on acoustic data pre-training and data augmentation to outperform classical ML classifiers in recognizing bee buzzing sounds. Under these conditions, the CNN models could lead to automating the taxonomic recognition of flower-visiting bees of blueberry crops. However, there is still room to improve the performance of CNN models by focusing on recording samples for poorly represented bee species. Automatic acoustic recognition associated with the degree of efficiency of a bee species to pollinate a particular crop would result in a comprehensive and powerful tool for recognizing those that best pollinate and increase fruit yields.

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds.

Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees-those that are efficient pollinators-is essential to improve the economic returns for farmers. To achieve this, it is important to know the identity of the visiting bees. Nevertheless, the traditional taxonomic identification of bees is not an easy task, requiring the participation of experts and the use of specialized equipment. Due to these limitations, the development and implementation of new technologies for the automatic recognition of bees become relevant. Hence, we aim to verify the capacity of Machine Learning (ML) algorithms in recognizing the taxonomic identity of visiting bees to tomato flowers based on the characteristics of their buzzing sounds. We compared the performance of the ML algorithms combined with the Mel Frequency Cepstral Coefficients (MFCC) and with classifications based solely on the fundamental frequency, leading to a direct comparison between the two approaches. In fact, some classifiers powered by the MFCC-especially the SVM-achieved better performance compared to the randomized and sound frequency-based trials. Moreover, the buzzing sounds produced during sonication were more relevant for the taxonomic recognition of bee species than analysis based on flight sounds alone. On the other hand, the ML classifiers performed better in recognizing bees genera based on flight sounds. Despite that, the maximum accuracy obtained here (73.39% by SVM) is still low compared to ML standards. Further studies analyzing larger recording samples, and applying unsupervised learning systems may yield better classification performance. Therefore, ML techniques could be used to automate the taxonomic recognition of flower-visiting bees of the cultivated tomato and other buzz-pollinated crops. This would be an interesting option for farmers and other professionals who have no experience in bee taxonomy but are interested in improving crop yields by increasing pollination.

Nectar Secretion of Floral Buds of Tococa guianensis Mediates Interactions With Generalist Ants That Reduce Florivory.

The specialised mutualism between Tococa guianensis and ants housed in its leaf domatia is a well-known example of myrmecophily. A pollination study on this species revealed that flowers in the bud stage exude a sugary solution that is collected by ants. Given the presence of this unexpected nectar secretion, we investigated how, where, and when floral buds of T. guianensis secret nectar and what function it serves. We studied a population of T. guianensis occurring in a swampy area in the Cerrado of Brazil by analyzing the chemical composition and secretion dynamics of the floral-bud nectar and the distribution and ultrastructure of secretory tissues. We also measured flower damage using ant-exclusion experiments. Floral bud nectar was secreted at the tip of the petals, which lack a typical glandular structure but possess distinctive mesophyll due to the presence of numerous calcium oxalate crystals. The nectar, the production of which ceased after flower opening, was composed mainly of sucrose and low amounts of glucose and fructose. Nectar was consumed by generalist ants and sporadically by stingless bees. Ant exclusion experiments resulted in significantly increased flower damage. The floral nectar of T. guianensis is produced during the bud stage. This bud-nectar has the extranuptial function of attracting generalist ants that reduce florivory. Pollen is the unique floral resource attracting pollinators during anthesis. Tococa guianensis, thus, establishes relationships with two functional groups of ant species: specialist ants acting against herbivory and generalist ants acting against florivory.