A deep learning based mobile application that identifies Colony Collapse Disorder in beehives.
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Phone Number: (925) 858-2725
Mailing Address: 3871 Appian St.
California: Pleasanton (94588)
Date You Started Your Project Started
Project Stage: Select the description below that best applies to your approach.
Start-Up (first few activities have happened)
1. The Problem: What problem are you helping to solve?
One of the most prominent issues currently facing our environment is bee mortality. As primary pollinators of more than one third of crops produced worldwide, these bees are critical to maintaining a balance in both our food supply and wildlife dependent on these plants. However, due to a phenomenon known as CCD (Colony Collapse Disorder) caused by the weakening of the insects’ immune system, these bees have been dying out by the millions.
2. Your Solution: How are you planning to solve this problem? Share your specific approach.
SafeHive is an innovative deep learning approach to make CCD diagnosis more accurate and easily accessible. It uses TensorFlow and a Convolutional Neural Network to take the weather, the bees’ food supply, and a picture of the hive as input from the user and output the risk percentage for CCD. In order to accomplish this, the mobile application first parses and separates the bees from the honeycomb in the hive image. It then utilizes TensorFlow to calculate the probability that they display certain signs of CCD, such as Deformed Wing Disorder (DWD) in the bees or Varroa Mite Infestation in the honeycomb. These probabilities, along with the weather and food source data, then get fed into the Convolutional Neural Network. Hidden layers in the network train their activation functions using a training dataset and, ultimately, output a percent that denotes the likelihood of the hive being affected by CCD in the present or the future. Having such predictive technology just a tap away on their smartphones will help many beekeepers prepare ahead of time for a potential outbreak and readjust their practices to prevent a similar situation from arising in the future.
3. Personal Journey: What’s the story behind why you decided to start this project?
CCD first caught my attention earlier this year in my AP Biology class, when we watched a video explaining how bees were rapidly disappearing due to the mysterious Colony Collapse Disorder. I was shocked by how much ambiguity remained around an issue of such large magnitude and how little was being done to address it. Drawn by curiosity, I continued researching into the disorder until I discovered that it was heavily influenced by several different factors, namely virus infestations, weather, and food source. It was here that I realized I could combine my newfound interest in hive protection with my long-time passion for artificial intelligence. After sifting through the pros and cons of many computer vision models and neural network structures, I finally settled on TensorFlow and a Convolutional Neural Network as the most reliable and accurate options for this project.
4. Selfie Elevator Pitch: Include 1-minute video that answers the following “I am stepping up to make change because...”
5. Example: Please walk us through a specific example of what happens when a person or group gets involved with your project.
SafeHive’s services are extremely self-explanatory and easily accessible. A user begins by installing the app onto a phone or tablet with access to the internet. To analyze a hive, the user first uploads an image in which both the bees and the honeycomb background are clearly visible. If the picture lacks clarity, they will be asked to replace it with a sharper image. Next, the user will answer several simple questions regarding the bees’ food source as prompted by the app. SafeHive then accesses the internet to obtain weather conditions specific to the photo’s location. The deep learning model analyzes these parameters to calculate the hive’s risk of contracting CCD and returns this likelihood in the form of a percentage to the user. This diagnosis will then allow the beekeeper to take measures to isolate or protect the hive from a future infestation.
6. The X Factor: What is different about your project compared to other programs or solutions already out there?
Due to the lack of a significant public or governmental push towards addressing bee mortality, there are currently no other applications available to assist beekeepers in diagnosing or preventing CCD. Since research in the field is still ongoing, scientists don’t have a concrete idea of how different factors contribute to the disorder. Deep learning models such as SafeHive, however, train themselves based on pre-fed data, so they are often more effective than even humans experts in finding correlations and trends from different variables.
7. Impact: How has your project made a difference so far?
SafeHive will be released to the public in the form of a mobile application, so the installation count will prove to be a reliable index of its impact. Considering that many beekeepers lose almost 50-90% of their colonies to CCD, it is evident that an increase in the number of users will be strongly correlated to a growth in the number of hives successfully diagnosed. The Department of Agriculture estimates that there are nearly 212,000 beekeepers in the United States alone, meaning that the potential worldwide reach of SafeHive will grow to be much larger. With such a significant opportunity to make a powerful difference around the globe, SafeHive’s novel approach will help stabilize the overall population of bees by supporting one individual beekeeper at a time.
8. What’s Next: What are your ideas for taking your project to the next level?
As of now, I am using a publicly available dataset of bee health from Kaggle for the preliminary training of the model. However, this dataset is limited in its relevance to CCD factors, making it difficult to achieve a high accuracy. To expand this data, I plan on contacting and partnering with individual beekeepers as well as California environmental agencies to obtain a larger and more reliable training set that includes a greater variety in potential virus infestations and more realistic food sources. Once SafeHive is prepared for public release, these connections will also prove to be beneficial in helping the application reach a broader audience and, subsequently, maximize its positive impact.
9. Which of the following types of expertise would be most useful for you?
10. Finances: If applicable, have you mobilized any of the following resources so far?
How did you hear about this challenge?