20.500.12556/DKUM-89463 DigiPig First developments of an automated monitoring system for body, head and tail detection in intensive pig farming The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%). pig welfare image processing object detection deep learning smart farming prašiči dobro počutje obdelava slik zaznavanje predmetov globoko učenje pametno kmetovanje true true true MDPI Angleški jezik Slovenski jezik Članek v reviji 2024-07-11 10:58:04 2024-07-11 11:22:47 2024-10-17 11:14:03 0000-00-00 00:00:00 2022 0 0 Str. 1-12 Št. 1, št. članka 2 Letn. 12 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2021-10-31 2021-12-12 2021-12-21 636.4:591.5:004.9 90755587 10.3390/agriculture12010002 2077-0472 Ocepek-2022-DigiPig__First_Developments_of_an.pdf Ocepek-2022-DigiPig__First_Developments_of_an.pdf 1 1D3C88769D55277552BAC28DFA6C7E5F 5f764c46de8c410324929defd2f4eeb89a2ca2c7d7918972d853db288fe4a123 f6616e65-3f66-11ef-a161-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=181374 https://doi.org/10.3390/agriculture12010002 0 10e20abe-3f67-11ef-a161-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=181375 Fakulteta za kmetijstvo in biosistemske vede 0 0 0