“Deep learning–based automated measurements of the scrotal circumference of Norwegian Red bulls from 3D images” seminar by QFRG & DSLab [27.02.2023]
We kindly invite to join the 31st seminar organised jointly by the Quantitative Finance Research Group and Data Science Lab.
During the coming meeting Joanna Bremer (Inland Norway University of Applied Sciences) and Michał Maj (Climatica) will present their research.
The meeting will take place on February 27th at 5 p.m. in hybrid mode: on-site in room B002 at the Faculty of Economic Sciences (Długa 44/50) and via Zoom platform.
Link to the meeting:
https://uw-edu-pl.zoom.us/j/92325152517?pwd=bGx5R0RVRXRtTk95YUZKNzNMa0o4dz09
Meeting ID: 923 2515 2517 Passcode: 844384
The meeting will be conducted in English. Please log in the latest at 4:50 p.m.
Joining a meeting implies consent to recording. Please turn off cameras and microphones during the presentation and send the questions to the speaker in the chat.
Presentation abstract:
Our study aimed to create an automated method for the measurement of the scrotal circumference (SC) of Norwegian Red bulls using 3D images of the scrotum based on convolutional neural networks. It is a major agricultural trend to automate measurements of different physiological and behavioural traits. Scrotal circumference is an essential part of the selection criteria for bulls in breeding programs. Traditionally circumference is measured manually with the use of scrotal tape. The study population was bull calves recruited for performance testing before the selection of bulls for semen production in the breeding program. Bulls were measured at four different time points: upon arrival in quarantine (Q) and thereafter at approximately 6, 9 and 12 months of age. Both 3D images and manual SC measurements were performed at all time points. In our approach, SC could be calculated without direct contact with the bull, using only 3D images and a simple, user–friendly application into which mentioned images are uploaded. The results show that SC measurements obtained using semantic segmentation are comparable with manual measurements. The mean prediction error was significantly different between age groups Q, 6, 9 and 12, and it was -3.07 cm, -3.02 cm, -1.79 cm and -1.11 cm, respectively. The results show a significant difference in the SC measurement error based on the image quality. For good prediction accuracy, we recommend capturing 3D images of the best quality defined in the paper for bulls older than 6 months, considering the light conditions.
