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A Machine Learning Based Approach to Study Morphological Features of Bees
John Yoo 1 , Md Zakir Hossain * 2, 3 , Khandaker Asif Ahmed * 4
1  Research School of Computing, The Australian National University (ANU), Canberra, ACT 2601, Australia
2  CSIRO Agriculture and Food, Black Mountain, Canberra, ACT 2601, Australia
3  Research School of Biology and Research School of Computing, ANU, Canberra, ACT 2601, Australia
4  CSIRO Land and Water, Black Mountain, Canberra, ACT 2601, Australia
Academic Editor: Andjeljko Petrović

10.3390/IECE-10607 (registering DOI)

Bees are the major pollinators of agricultural crops and due to numerous factors, the global
bee population is declining drastically. Identification and extraction of numerous body features of bees can allow us to understand the population dynamics and bee-hive health of an agricultural area. Morphological key-based bee studies are well established procedures for these tasks, which are time consuming and need critical knowledge about different bees species. Recently, numerous machine learning (ML) methods have been implemented on numerous insect species, but there is a scarcity of deep learning models for morphological studies of bees. In our current study, we applied ML methods to extract variants of class activation maps that visually display distinguishing morphological features of bees. We sourced an image data set of eleven different species of Bumblebee (Bombus sp.), Honey bee (Apis sp.) and Carpenter bee (Xylocopa sp.) from iNaturalist, curated and fine-tuned against fifteen state-of-the-art image classification models. An accuracy of 93.66% was obtained with a ResNest101e model, and including data augmentation improves the performance to the highest accuracy of 94.27%. We also compared the ML extracted visual features with traditional morphological key-based features and showed existing unsupervised ML models are error prone in numerous instances due to their focus on overall features, whereas manual methods benefited by focusing only on the main discriminating body features, showing a potential scope of improvement the existing models. Overall, our model will be implicated in bee-morphology based tasks of apiculture, such as distinguishing between healthy and parasitic bees, and classification tasks of similar insect species.

Keywords: Machine learning; Bees; Morphology; Feature extraction