Deep Learning-Enhanced Diagnosis of Sow Pregnancy Through Low-Frequency Ultrasound Imaging

Deep Learning-Enhanced Diagnosis of Sow Pregnancy Through Low-Frequency Ultrasound Imaging

The escalating demand for pork highlights the importance of swift and accurate pregnancy diagnosis in sows, a crucial factor in farm profitability. The prevalent use of low-frequency ultrasound devices in this context poses a challenge owing to the suboptimal resolution of the resultant images. This...

Full description

Saved in:
Bibliographic Details
Main Authors: Tae-kyeong Kim, Yo-Han Choi, Jun-Seon Hong, Hyun-Ju Park, Yong-Min Kim, Jo-Eun Kim, Ji-Hwan Lee, Soo-Jin Sa, Yong-Dae Jeong, Jin-Soo Kim, Hyun-chong Cho
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Animals
Subjects:
deep learning
low-frequency ultrasound
pregnant diagnosis
Online Access:https://www.mdpi.com/2076-2615/15/3/318
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The escalating demand for pork highlights the importance of swift and accurate pregnancy diagnosis in sows, a crucial factor in farm profitability. The prevalent use of low-frequency ultrasound devices in this context poses a challenge owing to the suboptimal resolution of the resultant images. This study introduces an innovative approach for sow pregnancy diagnosis using deep learning techniques to analyze low-frequency ultrasound images. Our methodology encompasses the development and comparative analysis of three distinct classification models: ViT-H, ConvNeXt-xlarge, and Xception. These models aim to improve diagnostic accuracy. AutoAugment was used to augment the data to expand the training dataset, thereby enhancing the robustness of the models under varied conditions. Results indicate a notable improvement in diagnostic performance, with the implementation of AutoAugment leading to significant achievements in the models, reflected by AUC values of 0.865, 0.856, and 0.866. These outcomes affirm the viability of deep learning in the effective management of sow pregnancies in livestock farms and suggest potential applications in broader animal husbandry contexts. This research marks a significant contribution to the evolution of agricultural technologies, presenting a scalable and efficacious solution for sow pregnancy diagnosis.
ISSN:2076-2615

Similar Items