How Will Machine Learning Transform Beehive Monitoring?

Key takeaways

Honey bees are crucial for ecological balance, impacting plant pollination and food production.
Global warming and agricultural practices have significantly contributed to the decline of honey bee populations.
Modern technology, including machine learning and ecoacoustics, is enhancing beekeeping practices and bee health monitoring.
Data collection through electronic monitoring systems is vital for understanding the health of bee colonies.

Just as bacteria are essential for life on earth, honey bees also play a vital role in ecology. Without them, it is said that almost all plants that pollinate, would be wiped out in a short period of time. Thanks to global warming and other issues such as parasitic attacks, industrial agriculture etc., honey bees have been declining in number. It will not come as a surprise if plants bear no fruit or vegetables if this is not looked into. In the recent years, beekeeping activities, as well as apiaries, have seen newer methods and techniques to improve bee sustainability. For example, the rise in computing and electronics paved the way for electronic beehive monitoring which is used to collect data surrounding bee health. Similarly, areas such as ecoacoustics use sensors and other elements to evaluate hostile environments for bees. In this article, we will try to reflect upon how acoustics, machine learning and deep learning have helped determine factors in honey bee colonies and hives. Many times, these have a direct impact on bees’ health. By automating them through ML and providing reference data through datasets, researchers and apiologists can transform beekeeping approaches altogether.

Going Electronic

Before we start with DL or ML in analysing beehives, let's take a look into electronic beehive monitoring (EBM) systems. The concept of EBM dates back to 2014 where it was used to analyse colony behaviour in bees without relying on intrusive techniques. Earlier studies included audio tools to evaluate bee noise that signalled activity in hives. Gradually, it expanded to measuring other factors such as weight, temperature and humidity in the hive. This required electronic instruments in place for measurement. All of this called for a comprehensive EBM system. A range of studies used different techniques. For example, a solar-powered EBM device called BeePi was developed to collect audio, temperature, and image data in different weather conditions. Another study used an audio EBM setup and MATLAB software to ascertain swarming periods in bees by observing swarming sounds. EBM systems help link dependent factors that affect bee life from hives effectively. This is the reason ML is explored in these systems.

Audio Beehive Monitoring With Deep Learning

In a study by Vladimir Kulyukin and team from the Utah State University, they developed a convolutional neural network (CNN) to classify audio samples of beehives amongst other noises. They also compared these CNNs with standard ML methods such as logistic regression, k-nearest neighbours, support vector machines and random forests. All of these are integrated with EBM system. The researchers consider two instances of datasets.

The first dataset named BUZZ1 has 10,260 audio samples for classification
The second dataset named BUZZ2 has 12,914 samples

Audio samples were actually collected from microphones placed in Langstroth hives in the experiment. They are primarily categorised into three distinctions

Bee buzzing (B)
Cricket chirping (C)
Ambient noise (N)

“We chose these categories for the following reasons. The B category is critical in profiling acoustic signatures of honeybee colonies to identify various colony stressors. The C category is fundamental in using audio classification as a logical clock because cricket chirping is cyclical, and, in Northern Utah, is present only from 11:00 p.m. until 6:00 a.m. The N category helps EBM systems to identify uniquely individual beehives because ambient noise varies greatly from location to location and from beehive to beehive at the same location.” In terms of performance in BUZZ1 dataset (both training and validation), CNNs performed on par with ML. But for the BUZZ2 dataset, CNNs showed superior performance compared to the ML methods. Accuracy was consistent on both training and validation, faring as much as 99 percent in classifying factors. ML methods, on the other hand, ranged from 90-95 percent. In fact, trained CNNs were tested on a low voltage Raspberry Pi computer. The spectrograms of the audio samples were also computed. This is also to demonstrate the power-efficiency in low power devices which makes CNNs attractive for implementation.

Conclusion

Although EBM systems are relatively new in the tech space, a lot could be achieved by combining them with ML and DL. Growing technology will allow non-intrusive techniques like the one mentioned above to make bee studies scalable. In addition, mediums such as sounds can tell a lot about bee activity, and narrowing on these through EBM will give an idea about the condition of bees on various factors regardless of geographical diversity.

Monitoring Beehives Through ML And Deep Learning Will Have Positive Ecological Ramifications

Key takeaways

Going Electronic

Audio Beehive Monitoring With Deep Learning

Conclusion

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