Deep learning-based classification models for beehive monitoring
Introduction
Honey bees are eusocial flying insects within the genus Apis. They are the main actors in honey production and pollination. Though 7 to 11 species of honey bees are recognized historically, only two of these species have been truly domesticated for honey production: the western (or European) honey bee (Apis mellifera) and the eastern honey bee (Apis cerana) (Engel, 1999). The western honey bee is more common worldwide, while the eastern honey bee is found mainly in South Asia (Beaurepaire et al., 2020).
Unfortunately, these creatures, which are of great importance to nature and humanity, face various threats such as pests, diseases, honey robberies, and changing landscapes (Andrews, 2019; Beaurepaire et al., 2020). Among all these threats, Varroa destructor, an ectoparasitic mite, is one of the most significant ones that can lead to the collapse of bee colonies if left untreated (Mondet et al., 2020; Ramsey et al., 2019; Traynor et al., 2020). Varroa attaches to the body of the honey bee and weakens it by sucking the fat body tissue (Ramsey et al., 2019). Varroa also transmits several viruses, such as acute bee paralysis virus and the deformed wing virus, that may cause severe health issues in beehives (Beaurepaire et al., 2020; Ogihara et al., 2020b). Robber bees pose another threat as they gain their food by plundering the hives of the other bees instead of visiting the flowers (von Zuben et al., 2016). Also, small hive beetles may cause severe damage to the colonies and endanger beekeeping (Schafer et al., 2019). Beehives also attract other insects, such as ants (Nouvian et al., 2016).
Because of the abovementioned problems, beehive monitoring is vital for beekeepers to take appropriate countermeasures promptly. In traditional beekeeping, there are several methods for beehive monitoring. For example, a roll test with powdered sugar or roasted soybean flour is one of the common methods used to detect Varroa destructor infestations in beehives (Noël et al., 2020; Ogihara et al., 2020a). Visual observation is another traditional method to detect unintended beetles. However, these methods are time-consuming and not very efficient at all. Using signal processing, computer vision, and machine learning techniques, rather than the conventional methods, can be faster and much more effective in beehive monitoring.
Hence, in our work, deep learning-based classification models are proposed for beehive monitoring. The proposed models particularly classify honey bee images captured at beehives and can recognize different conditions, such as healthy bees, pollen-bearing bees, and certain abnormalities, such as Varroa parasites, ant problems, hive robberies, and small hive beetles. The models utilize transfer learning with seven different pre-trained deep neural networks (DNNs) and also a support vector machine classifier with deep features, shallow features, and both deep and shallow (hereinafter referred to as deep+shallow) features extracted from these DNNs. Three benchmark datasets, consisting of a total of 19,393 honey bee images for different conditions, were used to train and evaluate the models. A thorough experimental analysis was carried out to assess the models in terms of classification performance and processing time. An extensive experimental work revealed that the proposed models can recognize different conditions as well as abnormalities with significantly high performance and surpass the previous works.
The rest of the paper is organized as follows: Section 2 expresses the related work, Section 3 describes the honey bee image datasets and introduces the proposed classification models. In Section 4, the experimental work and related results are presented and discussed. Also, a comparison of this work against the literature is provided in terms of various aspects, including dataset size, number of classes, features, classification method, application field, classification performance, and processing time. Finally, some concluding remarks and future directions are given in Section 5.
Section snippets
Related work
In recent years, a limited number of efforts have been made to monitor beehives and bees using various image processing, audio processing, and machine learning techniques on still images, video streams, and audio recordings. In (Santana et al., 2014), a reference process was proposed to automate the identification of bee species based on wing images and digital image processing techniques. In (Babic et al., 2016), a system was proposed to detect pollen-bearing honey bees from surveillance
Materials and methods
In this section, the utilized image datasets are first described. Then, the proposed classification models are elaborated.
Results and discussions
The performances of the proposed classification models were evaluated by a comprehensive experimental work. As mentioned earlier, the test parts of the datasets introduced in the previous section were used for the performance evaluation. The experiments were carried out on a workstation equipped with an Intel(R) Xeon(R) E5–2643 v2 3.50 GHz CPU and 128 GB of RAM. The results of the experimental work for each dataset and the comparison against the related works are given in the following
Conclusions
While honey bees have an important role in the ecosystem, they face many problems, including parasites, ants, hive beetles, and robberies. Some of these problems could even lead to the collapse of colonies. Unfortunately, most of the traditional methods for solving these problems are very time-consuming and not effective at all. Therefore, there is a need for methods that would enable fast and effective beehive monitoring.
In this paper, four new image classification models were proposed to
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Competing Interest
None.
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