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

• Main Authors: da Silva, N.F.F (Author), de Cássia Souza Araújo, P. (Author), Mesquita, F.N (Author), Mesquita-Neto, J.N (Author), Ribeiro, A.P (Author), Rosa, T.C (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: acoustics; Acoustics; agricultural worker; algorithm; Algorithms; animal; Animals; article; bee; Bees; biology; classification; classifier; Computational Biology; crop; Crops, Agricultural; flower; Flowers; growth, development and aging; harvest; human; human cell; Lycopersicon esculentum; machine learning; Machine Learning; nonhuman; physiology; pollination; Pollination; randomized controlled trial (topic); taxonomic identification; tomato; ultrasound
• Online Access: View Fulltext in Publisher

 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 from 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. Copyright: © 2021 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.