Deep learning-based classification models for beehive monitoring

Highlights

• Deep learning-based classification models are proposed for beehive monitoring.

• The models particularly classify honey bee images captured at beehives.

• The models can accurately recognize different conditions as well as abnormalities.

Abstract

Honey bees are not only the fundamental producers of honey but also the leading pollinators in nature. While honey bees play such a vital role in the ecosystem, they also face a variety of threats, including parasites, ants, hive beetles, and hive robberies, some of which could even lead to the collapse of colonies. Therefore, early and accurate detection of abnormalities at a beehive is crucial to take appropriate countermeasures promptly. In this paper, deep learning-based image classification models are proposed for beehive monitoring. The proposed models particularly classify honey bee images captured at beehives and 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 pre-trained deep neural networks (DNNs) and also a support vector machine classifier with deep features, shallow features, and both deep and 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. The results of the extensive experimental work revealed that the proposed models can recognize different conditions as well as abnormalities with an accuracy of up to 99.07% and stand out as good candidates for smart beekeeping and 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.