Honeybee colonies throughout North America have declined precipitously due to parasites, pesticides, and poor nutrition over the past two decades. Monitoring hive health autonomously assists beekeeper efforts. We developed a model which automatically detects events in bee hive weight data assisting data collection efforts improving data quality for future machine learning models to be developed.
Pesticides, parasites, and poor nutrition, has led to the decline of honeybee colonies throughout North America. A number of methods have been proposed to combat the problem, with one here at Grand Valley State University (GVSU) focusing on collecting hive weight data identifying potential issues through data analytics. Currently, “citizen scientist” beekeepers participate by collecting weight data from their hives through the Bee Informed Partnership (BIP). Using Python3, Bokeh, SciKit learn and Pandas we were able to produce a model using linear regression that could predict patterns in weight data. Our short term goal for the project was to create a model that could predict events and windows of time where events could have occured to improve the data quality and user engagement. The ultimate long term goal of this project is to predict what kind of event occurred such as adding food to the hive, harvesting honey, swarming events, and even parasite infestation.