Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine
Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die withou...
Main Author: | Amlathe, Prakhar |
---|---|
Format: | Others |
Published: |
DigitalCommons@USU
2018
|
Subjects: | |
Online Access: | https://digitalcommons.usu.edu/etd/7050 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=8156&context=etd |
Similar Items
-
Feature Selection and Analysis for Standard Machine Learning Classification of Audio Beehive Samples
by: Gupta, Chelsi
Published: (2019) -
Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples
by: Vladimir Kulyukin, et al.
Published: (2018-09-01) -
Detection of Pesticides in Active and Depopulated Beehives in Uruguay
by: Horacio Heinzen, et al.
Published: (2011-09-01) -
Power Analysis of Continuous Data Capture in BeePi, a Solar- Powered Multi-Sensor Electronic Beehive Monitoring System for Langstroth Beehives
by: Shah, Keval
Published: (2017) -
Audio, Image, Video, and Weather Datasets for Continuous Electronic Beehive Monitoring
by: Vladimir Kulyukin
Published: (2021-05-01)