SBIR Phase I: Data Analytics on Honeybee Hives Using IoT Sensor Data

SBIR Phase I: Data Analytics on Honeybee Hives Using IoT Sensor Data

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1746862
Agency Tracking Number: 1746862
Amount: $224,972.00
Phase: Phase I
Program: SBIR
Awards Year: 2018
Solicitation Year: 2017
Solicitation Topic Code: I
Solicitation Number: N/A
Small Business Information
415 S Dunn St, Apt 4, Bloomington, IN, 47401
DUNS: 080228218
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Ellie Symes
 (614) 440-8060
 ellie.symes@thebeecorp.com
Business Contact
 Ellie Symes
Phone: (614) 440-8060
Email: ellie.symes@thebeecorp.com
Research Institution
N/A
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a healthier honeybee population to increase food security. According to the Food and Agriculture Organization, annual production must increase by 60% from 2007 to produce enough food for an estimated 9.1 billion people by 2050, which will be impossible without honeybee pollination (Food and Agriculture Organization, 2012). Honeybee populations have declined precipitously for the past 60 years?a total loss of 3 million hives (National Agricultural Statistics Service, 2007). Annual hive losses cost the U.S. economy $2 billion (National Agricultural Statistics Service, 2016; The White House, 2014). This project aims to enhance scientific understanding of the problem by finding practical solutions to hive loss for beekeepers, advancing IoT applications in beekeeping, and automating data analysis for beekeepers. Researchers and experts suspect the declines are due to multiple factors, but research is limited by a lack of technology tracking the hives on a large scale. Researchers' inability to perform analysis on a large, diverse dataset has obstructed their ability to draw conclusions on causes of hive loss and drive innovation on traditional beekeeping methods.  This proposed project will advance Internet of Things applications in beekeeping by automating data analysis for beekeepers through algorithm building. Several studies built models to describe actions inside the hive. However, these are built on limited data or are theoretical. Henry et al., 2016 mentioned the need for predictive algorithms to be expanded to larger data sets to reduce hive loss. Data driven beekeeping is in its infancy as an industry, but based on the success in the AgTech space, data monitoring will be a necessary step in solving colony health problems. The project will take a database of over 4,000 hives to improve research models for practical monitoring. This database will be paired with sensor and monitoring data from project hives. Through the project hives the company will confirm the sensor quality for commercialization. The company will create a baseline model of a healthy hive to detect anomalies. These anomalies will be worked into a predictive model to detect problems related to pests and diseases in the hive. The company will look for broad detection of a threatened hive, then drill down into specific problems (like Varroa mites). These finds will be included in monitoring products offered to beekeepers.

* Information listed above is at the time of submission. *

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