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Anna Yang

Anna Yang

2023 Davidson Fellow
$10,000 Scholarship

Age: 18
Hometown: Campbell, CA

Engineering: “Smart Bee Colony Monitor: Internet of Things Device & Fusion Convolutional Neural Network for Queen Assessment”

About Anna

Hi! I’m Anna, and I’m from the Bay Area, California. Beyond research, I am passionate about writing, and  in my free time, I love listening to Lana Del Rey, teaching volleyball to young athletes, caring for the community beehive I started at Taylor Street Farm in San Jose, traveling, and wandering around art museums.

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"To be named a 2023 Davidson Fellow is such an honor. I am so grateful that my research, alongside the work of my class of fellows, is being highlighted for its incredible positive impact on the world. I can’t wait to join this inspirational community of fellows, where we will support and cultivate each other’s creativity."

Project Description

This research project addresses queen failure, one of the leading causes of bee colony collapse. Some beekeepers will introduce a new, purchased queen to the colony once a queen has died— which the colony usually rejects at first, before they become familiar and accept her before she can be released (a process taking 5 days and involving guesswork from the beekeeper). Thus, I developed the Smart Bee Colony Monitor — a system that uses a custom data collection device and machine learning to determine queen presence and acceptance. The results showed a 91.8% accuracy for queen presence evaluation, demonstrating the potential for this system to be mass-produced for beekeepers worldwide.

Deeper Dive

As a beekeeper, I have always been aware of the rapid decline in bee population across the world. But, from online research, I found that one of the leading causes of colony mortality for both commercial bee farms and hobbyists was queen failure, which included queen death and the inability of the hive to produce a new queen naturally. After reading an article about the “queenless roar,” I was inspired to begin this project. It explained that queenless colonies would emit a specific buzz, which some experienced beekeepers were able to identify. However, as an amateur beekeeper, I was far from sensitive to the sound. So, using a tool that I was familiar with, machine learning, I wanted to create an algorithm that could help beekeepers monitor the presence of their queens without needing to take the time to acquire the listening skill. I organized the research into three components: the IoT data collection device, the collection of high-quality and diverse data, and the development and experimentation of machine learning models for queen assessment.

My mentor, Professor Huang, advised me to include queen acceptance/rejection from the hive, since a common problem in the beekeeping community was determining when to release a new queen from her cage in the hive so that she wouldn't be attacked by the colony. If released too early, the queen could be rejected and killed. If released too late, the colony could already be too weak without an active queen laying eggs. Some of my greatest difficulties were in the data collection, as I was working alongside nature, which is very difficult to control. On two separate occasions, the queen bees were rejected by the hive after re-introduction and flew away, unable to be found. In my first collection of a week of queenright data, when I had not yet considered all possibilities, I discovered that the hive I believed to be queenright had actually lost the queen over the course of my recording, and had made more than ten emergency queen cells. This especially had come as a shock to me, because it had been one of my most robust hives. To overcome these difficulties, I simply tried and tried again. Almost none of the data collection cycles went completely smoothly, but I was able to piece together the valid data and eliminate the indeterminate data to form the 7.1k sample dataset.

Through publishing my open-access dataset, I hope that other researchers will continue investigating queen acceptance and sound, making advancements inspired by my work. This research is only the beginning for a solution to a common beekeeping problem, queen failure, which I hope to continue developing. Using the hardware and machine learning developed in this research, I want to assist all beekeepers— irrelevant of honey bee strains, location, occupation (commercial or hobbyist), and season. This research covers the basis for the realization of this goal.


What is your favorite hobby?

Hiking in the morning or at sunset never fails to make my day.

What is your favorite tradition or holiday?

I love helping my grandma fold dumplings for dinner every time I visit Beijing.

If you could magically become fluent in any language, what would it be?

Proto-Elamite — the still-undeciphered written language of 3000 BC in present-day Iran.

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In The News

San Francisco – The Davidson Fellows Scholarship Program has announced the 2023 scholarship winners. Among the honorees is 18-year-old Anna Yang of Campbell. Yang won a $10,000 scholarship for her project, Smart Bee Colony Monitor: Internet of Things Device & Fusion Convolutional Neural Network for Queen Assessment. She is one of only 21 students across the country to be recognized as a 2023 scholarship winner.

Download the full press release here