TY - GEN
T1 - Advanced Computer Vision Techniques for Reliable Gender Determination in Budgerigars (Melopsittacus Undulatus)
AU - Denknalbant, Atalay
AU - Cemalcilar, Efe Ilhan
AU - Ahangari, Majid
AU - Saidburkhan, Abdussamat
AU - Ghazani, Alireza Zirak
AU - Arican, Erkut
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Determining the gender of budgerigars by observing cere color can be unreliable and subjective, especially in young birds or those with atypical color variations. In this study, a comprehensive approach to Budgerigar (Melopsittacus undulatus) Gender Recognition was presented by applying advanced techniques of computer vision. Diverse methodologies were added to set up a reliable system to identify the gender of budgerigars, focusing on images of yellow, white, green, and blue budgerigars based on cere color. A dataset was prepared, and multiple deep learning models, including YOLOvge, YOLOv8x, YOLOv8m, YOLOv5x, YOLOv5m, VGG19, ResNet152, EfficientNet, and AlexNet, were experimented on, focusing on object detection to locate the cere and determine gender accurately. The YOLOvge state-of-the-art model showed the best performance. The results indicate wide implications for avian research, providing a new tool for gender identification for budgerigars and possibly other parakeet species.
AB - Determining the gender of budgerigars by observing cere color can be unreliable and subjective, especially in young birds or those with atypical color variations. In this study, a comprehensive approach to Budgerigar (Melopsittacus undulatus) Gender Recognition was presented by applying advanced techniques of computer vision. Diverse methodologies were added to set up a reliable system to identify the gender of budgerigars, focusing on images of yellow, white, green, and blue budgerigars based on cere color. A dataset was prepared, and multiple deep learning models, including YOLOvge, YOLOv8x, YOLOv8m, YOLOv5x, YOLOv5m, VGG19, ResNet152, EfficientNet, and AlexNet, were experimented on, focusing on object detection to locate the cere and determine gender accurately. The YOLOvge state-of-the-art model showed the best performance. The results indicate wide implications for avian research, providing a new tool for gender identification for budgerigars and possibly other parakeet species.
KW - Budgerigar
KW - Computer Vision
KW - Deep Learning
KW - Gendei Determination
KW - Melopsittacus undulatus
KW - Object Detection
UR - https://www.scopus.com/pages/publications/85215503156
U2 - 10.1109/UBMK63289.2024.10773570
DO - 10.1109/UBMK63289.2024.10773570
M3 - Conference contribution
AN - SCOPUS:85215503156
T3 - UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering
SP - 863
EP - 868
BT - UBMK 2024 - Proceedings
A2 - Adali, Esref
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Computer Science and Engineering, UBMK 2024
Y2 - 26 October 2024 through 28 October 2024
ER -