Treffer: Bee Species Identification: Improving Population Monitoring Techniques 
Title:
Bee Species Identification: Improving Population Monitoring Techniques
Authors:
Publisher Information:
eScholarship, University of California 2024-06-03
Document Type:
E-Ressource
Electronic Resource
Index Terms:
classification, neural network, perspective correction, species identification, computer vision, wing morphology, Python, ARuCO markers, preprocessing pipeline, VGG-16, web application, linear discriminant analysis, spectral embedding, landmarks, k-nearest neighbors, unsupervised learning, data science, publication
URL:
Availability:
Open access content. Open access content
public
public
Note:
application/pdf
Other Numbers:
CDLER oai:escholarship.org:ark:/13030/qt7z6734gt
qt7z6734gt
1449593047
qt7z6734gt
1449593047
Contributing Source:
UC MASS DIGITIZATION
From OAIster®, provided by the OCLC Cooperative.
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1449593047
Database:
OAIster
Weitere Informationen
This project aims to mitigate the critical decline in bee populations, essential for crop pollination and food security. With a shortage of taxonomic specialists to identify the vast array of bee species, the project's goal is to enhance the monitoring of population changes through an automated classification system. Utilizing a dataset of bee wing images, the project aims to develop a computational pipeline to identify species based on their unique wing vein patterns. This approach not only supports bee conservation efforts but also expands our understanding of complex geometric variations in nature, offering wider applications in biological research. This poster was presented at the UCSB Data Science Capstone showcase in 2024.