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An end-to-end beehive monitoring solution enabled by the Helium Network and deep learning.

Bees are critical to the livelihood of our ecosystem, but unfortunately in the past decade, bee populations have decreased by 30% [1]. In addition to the ecological impact, this instability poses an economic threat to the commercial honey bee pollination industry, which is valued at over $10 billion annually in the U.S. alone [2]. Much of the decline is attributed to a complicated phenomenon known as Colony Collapse Disorder (CCD), which results in the hive’s rapid abandonment of their queen, but the causes of CCD are not well understood. Whether you are an individual hobbyist or a commercial farmer who relies on large-scale pollination, monitoring your hive with simple sensory data can help beekeepers detect problematic trends in colony health. Our project, known as “LongHive”, is a full-service infrastructure for beehive maintenance, enabled by the Helium Network and Deep Learning (DL). Data-driven beekeepers can install our LongHive system, which fits underneath standard beehives and includes a suite of relevant sensors, a pre-trained convolutional neural network (CNN) for classifying the hive’s acoustic signatures, and a web-based dashboard for easy visualization of the transient signals.

The LongHive infrastructure is designed to facilitate a collaborative community forum (where users share data to improve DL models and general beekeeping insights), but the hardware operates within a modular framework to meet the specific needs of each beekeeper. Our goal is to help beekeepers make the most out of their time and reduce the frequency of intrusive hive inspections while still detecting problems within the hive. This is done by flagging problematic trends in the data, which could represent major issues like CCD, hive robbery, or missing queens. Our competition uses WiFi for connectivity, which has a limited range and consumes a significant amount of power, but the Helium Network enables low-power devices that can operate in a much more remote environment. We combine edge computing and a pre-trained network to circumvent the most glaring constraint of LoRaWAN – low transaction throughput – in our DL classifier. A Raspberry Pi bears the computational burden locally, so only the network output (the classification) needs to be transmitted over LongFi.

The LongHive Sensor Suite
In our review of relevant literature and existing commercial solutions, we found a slew of passive sensors that have proven to give some indication of hive health. First and foremost, we want to provide beekeepers with real-time data that they will use to augment their existing heuristics and improve productivity. Variation in hive weight is a sign of honey production and population. Temperature is a simple, but critical source of information; bees like to keep very precise thermal conditions for optimal hive development. In fact, they have fascinating mechanisms in place for maintaining this delicate homeostasis: when the hive is too hot, they fan their wings to increase convective cooling; when it is too cool, they generate heat by vibrating their flight muscles. Similarly, beekeepers must keep an eye on the relative humidity in their hive - eggs cannot hatch when it’s too dry, but damp conditions can be a sign of mold or disease. Carbon dioxide is released into the hive as a byproduct of honey production. Thus, a lack of proper ventilation can result in CO2 poisoning and other maladies. Beekeepers are responsible for making it as easy as possible for their hives to maintain this balance by making tweaks to airflow and insulation. We also found a lot of literature suggesting that the acoustic signals emitted by a hive can be a rich source of information, but it will take a more complex processing pipeline to make sense of it (more on this later).”

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