Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs. As an integral component of electronic beehive monitoring, audio beehive monitoring has the potential to automate the identification of various stressors for honeybee colonies from beehive audio samples. In this investigation, we designed several convolutional neural networks and compared their performance with four standard machine learning methods (logistic regression, k-nearest neighbors, support vector machines, and random forests) in classifying audio samples from microphones deployed above landing pads of Langstroth beehives. On a dataset of 10,260 audio samples where the training and testing samples were separated from the validation samples by beehive and location, a shallower raw audio convolutional neural network with a custom layer outperformed three deeper raw audio convolutional neural networks without custom layers and performed on par with the four machine learning methods trained to classify feature vectors extracted from raw audio samples. On a more challenging dataset of 12,914 audio samples where the training and testing samples were separated from the validation samples by beehive, location, time, and bee race, all raw audio convolutional neural networks performed better than the four machine learning methods and a convolutional neural network trained to classify spectrogram images of audio samples. A trained raw audio convolutional neural network was successfully tested in situ on a low voltage Raspberry Pi computer, which indicates that convolutional neural networks can be added to a repertoire of in situ audio classification algorithms for electronic beehive monitoring. The main trade-off between deep learning and standard machine learning is between feature engineering and training time: while the convolutional neural networks required no feature engineering and generalized better on the second, more challenging dataset, they took considerably more time to train than the machine learning methods. To ensure the replicability of our findings and to provide performance benchmarks for interested research and citizen science communities, we have made public our source code and our curated datasets.

Omnidirectional bee traffic is the number of bees moving in arbitrary directions in close proximity to the landing pad of a given hive over a given period of time. Video bee traffic analysis has the potential to automate the assessment of omnidirectional bee traffic levels, which, in turn, may lead to a complete or partial automation of honeybee colony health assessment. In this investigation, we proposed, implemented, and partially evaluated a two-tier method for counting bee motions to estimate levels of omnidirectional bee traffic in bee traffic videos. Our method couples motion detection with image classification so that motion detection acts as a class-agnostic object location method that generates a set of regions with possible objects and each such region is classified by a class-specific classifier such as a convolutional neural network or a support vector machine or an ensemble of classifiers such as a random forest. The method has been, and is being iteratively field tested in BeePi monitors, multi-sensor electronic beehive monitoring systems, installed on live Langstroth beehives in real apiaries. Deployment of a BeePi monitor on top of a beehive does not require any structural modification of the beehive’s woodenware, and is not disruptive to natural beehive cycles. To ensure the replicability of the reported findings and to provide a performance benchmark for interested research communities and citizen scientists, we have made public our curated and labeled image datasets of 167,261 honeybee images and our omnidirectional bee traffic videos used in this investigation.

The well-being of a honeybee (Apis mellifera) colony depends on forager traffic. Consistent discrepancies in forager traffic indicate that the hive may not be healthy and require human intervention. Honeybee traffic in the vicinity of a hive can be divided into three types: incoming, outgoing, and lateral. These types constitute directional traffic, and are juxtaposed with omnidirectional traffic where bee motions are considered regardless of direction. Accurate measurement of directional honeybee traffic is fundamental to electronic beehive monitoring systems that continuously monitor honeybee colonies to detect deviations from the norm. An algorithm based on digital particle image velocimetry is proposed to measure directional traffic. The algorithm uses digital particle image velocimetry to compute motion vectors, analytically classifies them as incoming, outgoing, or lateral, and returns the classified vector counts as measurements of directional traffic levels. Dynamic time warping is used to compare the algorithm’s omnidirectional traffic curves to the curves produced by a previously proposed bee motion counting algorithm based on motion detection and deep learning and to the curves obtained from a human observer’s counts on four honeybee traffic videos (2976 video frames). The currently proposed algorithm not only approximates the human ground truth on par with the previously proposed algorithm in terms of omnidirectional bee motion counts but also provides estimates of directional bee traffic and does not require extensive training. An analysis of correlation vectors of consecutive image pairs with single bee motions indicates that correlation maps follow Gaussian distribution and the three-point Gaussian sub-pixel accuracy method appears feasible. Experimental evidence indicates it is reasonable to treat whole bees as tracers, because whole bee bodies and not parts thereof cause maximum motion. To ensure the replicability of the reported findings, these videos and frame-by-frame bee motion counts have been made public. The proposed algorithm is also used to investigate the incoming and outgoing traffic curves in a healthy hive on the same day and on different days on a dataset of 292 videos (216,956 video frames).