TY - JOUR
T1 - Robust piglet nursing behavior monitoring through multi-modal fusion of computer vision and ambient floor vibration
AU - Dong, Yiwen
AU - Song, Zihao
AU - Codling, Jesse R.
AU - Rohrer, Gary
AU - Miles, Jeremy
AU - Sharma, Sudhendu
AU - Brown-Brandl, Tami
AU - Zhang, Pei
AU - Noh, Hae Young
N1 - This work was funded in part by the U.S. National Science Foundation (under grant numbers NSF-CMMI-2026699 ), Stanford Blume Fellowship, and Cisco Systems. The views and conclusions contained here are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of any University, the National Science Foundation, or the United States Government or any of its agencies. The USDA prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual’s income is derived from any public assistance program (Not all prohibited bases apply to all programs.). Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at (202) 720–2600 (voice and TDD). USDA is an equal opportunity provider and employer.
This work was funded in part by the U.S. National Science Foundation (under grant numbers NSF-CMMI-2026699), Stanford Blume Fellowship, and Cisco Systems. The views and conclusions contained here are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of any University, the National Science Foundation, or the United States Government or any of its agencies. The USDA prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual's income is derived from any public assistance program (Not all prohibited bases apply to all programs.). Persons with disabilities who require alternative means for communication of program information (Braille, large print, audiotape, etc.) should contact USDA's TARGET Center at (202) 720–2600 (voice and TDD). USDA is an equal opportunity provider and employer.
PY - 2025/11
Y1 - 2025/11
N2 - Nursing is a critical activity during the lactation period of swine farming. Continuous monitoring of piglet nursing behavior during the lactation period is essential to informing animal caretakers about the health status of piglets to reduce the mortality rate, maximize lactational growth, and improve animal welfare. Traditional approaches rely on manual observation and wearable devices, which are labor-intensive and can cause discomfort to the animals. Recent advancement in computer vision and ambient vibration sensing enables non-contact piglet nursing monitoring: The computer vision approach captures piglet location but has limited observation of their detailed movement due to lighting, resolution, and visual obstruction constraints; the ambient vibration sensing approach captures piglet movement patterns but has limited location information. In this study, a novel approach to integrate these two complementary sensing modalities is developed for robust piglet nursing behavior monitoring during the lactation period. This study leverages the state-of-the-art Segment Anything Model (SAM) to first convert images into sparse representations of piglet behaviors and then combine with ambient vibration to collaboratively infer piglet nursing pattern and intensity. This new approach enables piglet nursing monitoring with much lower computing and storage requirements than conventional computer vision methods, making it more practical for farm settings. Real-world experiments were conducted at a pig farm for continuous vision and vibration monitoring of 8 pens over 3 farrowing cycles. This study has a 97% accuracy in classifying 5 nursing stages, representing a significant 3× and 3.8× error reduction compared to the baseline method using only vision or vibration data, respectively. The multi-modal fusion approach leads to an efficient, robust, and accurate piglet nursing model that can immediately inform caretakers of issues that arise during this crucial time point of a piglet's life.
AB - Nursing is a critical activity during the lactation period of swine farming. Continuous monitoring of piglet nursing behavior during the lactation period is essential to informing animal caretakers about the health status of piglets to reduce the mortality rate, maximize lactational growth, and improve animal welfare. Traditional approaches rely on manual observation and wearable devices, which are labor-intensive and can cause discomfort to the animals. Recent advancement in computer vision and ambient vibration sensing enables non-contact piglet nursing monitoring: The computer vision approach captures piglet location but has limited observation of their detailed movement due to lighting, resolution, and visual obstruction constraints; the ambient vibration sensing approach captures piglet movement patterns but has limited location information. In this study, a novel approach to integrate these two complementary sensing modalities is developed for robust piglet nursing behavior monitoring during the lactation period. This study leverages the state-of-the-art Segment Anything Model (SAM) to first convert images into sparse representations of piglet behaviors and then combine with ambient vibration to collaboratively infer piglet nursing pattern and intensity. This new approach enables piglet nursing monitoring with much lower computing and storage requirements than conventional computer vision methods, making it more practical for farm settings. Real-world experiments were conducted at a pig farm for continuous vision and vibration monitoring of 8 pens over 3 farrowing cycles. This study has a 97% accuracy in classifying 5 nursing stages, representing a significant 3× and 3.8× error reduction compared to the baseline method using only vision or vibration data, respectively. The multi-modal fusion approach leads to an efficient, robust, and accurate piglet nursing model that can immediately inform caretakers of issues that arise during this crucial time point of a piglet's life.
KW - Computer vision
KW - Multi-modal
KW - Nursing
KW - Precision livestock farming
KW - Structural vibration
UR - https://www.scopus.com/pages/publications/105012817181
UR - https://www.scopus.com/pages/publications/105012817181#tab=citedBy
U2 - 10.1016/j.compag.2025.110804
DO - 10.1016/j.compag.2025.110804
M3 - Article
AN - SCOPUS:105012817181
SN - 0168-1699
VL - 238
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 110804
ER -