An Empirical and Theoretical Investigation of Random Reinforced Forests and Shallow Convolutional Neural Networks

For many years, the global population of honey bees has been decreasing due to inconclusive reasons resulting in the syndrome Colony Collapse Disorder (CCD). This syndrome has been plaguing bees and affecting commercial agriculture pollination since 1998. Many researchers have suggested that pesticides, in-hive chemicals, pathogens, etc., might be the causes of CCD. Researchers also believe that any changes in a beehive can disturb the bees, which may negatively affect their health. Honey bees are the most vital among all the animal pollinators contributing to approximately 30% of the world’s commercial pollination services. As they are of keystone importance to their respective ecosystems, monitoring their hives is crucial for understanding the effects of CCD and enabling beekeepers to maintain the health of their hives. As beekeepers cannot monitor their hives continuously, electronic beehive monitoring (EBM) can help them keep an eye on their hives. EBM extracts the videos, audios, temperature using cameras, microphones, sensors for observing the forager traffic (incoming and outgoing flow of the bees through the hive) to track food and nectar availability, following the sounds of the buzzing, and monitoring the abrupt temperature changes. EBM reduces the number of invasive inspections and transportation costs incurred for traveling to the beehive location. This research proposes a new technique using reinforcement learning, a method based on a reward/punishment strategy and aims at providing both accurate and energy efficient classification techniques to improve individual bee recognition in bee traffic videos

Automated Collection Of Honey Bee Hive Data Using The Raspberry Pi

In recent years beekeepers have faced significant losses to their populations of managed honey bees, a phenomenon known as Colony Collapse Disorder (CCD). Many researchers are studying CCD, attempting to determine its cause and how its effects can be mitigated. Some research efforts have focused on the analysis of bee hive audio and video recordings to better understand the behavior of bees and the health of the hive. To provide data for this research, it is important to have a means of capturing audio, video, and other sensor data, using a system that is reliable, inexpensive, and causes minimal disruption to the bees’ behavior. This thesis details the design and implementation of a data collection system, known as BeeMon, which is based around the Raspberry Pi. This system automatically captures sensor data and sends it to a remote server for analysis. With the ability to operate continuously in an outdoor apiary environment, it allows for constant, near real-time data collection. The results of several years of real world operation are discussed, as well as some research that has used the data collected

Implementation of an Automated Image Processing System for Observing the Activities of Honey Bees

This research designed and implemented an automated system to collect data on honey bees using computer science techniques. This system utilizes image processing techniques to extract data from the videos taken in front or at the top of the hive’s entrance. Several web-based applications are used to obtain temperature and humidity data from National weather Service to supplement the data that are collected at the hive locally. All the weather data and those extracted from the images are stored in a MySQL database for analysis and accessed by an iPhone App that is designed as part of this research