T Cao, X Li, X Liu, H Liang, H Wang, D Xu - Applied Sciences, 2024 - mdpi.com
Aiming to address the problem that the existing methods for detecting sow backfat thickness are stressful, costly, and cannot detect in real time, this paper proposes a non-contact …
M Ergin, Ö Koşkan - Tropical Animal Health and Production, 2025 - Springer
The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age …
N Shchepkina, A Chandramauli, S Ahuja… - BIO Web of …, 2024 - bio-conferences.org
This extensive experimental research provides strong empirical proof of the revolutionary power of deep learning algorithms when integrated into Industry 5.0. Convolutional Neural …
X Zong, Y Li, K Feng - International Conference on Computer …, 2024 - spiedigitallibrary.org
With the development of information technology, most domestic companies mainly use Internet recruitment methods for talent recruitment. Various Internet platforms have a large …
R Dabas, M Kumar, S Ansari… - Bio vet innovator …, 2024 - researchgate.net
The significance of back fat thickness (BFT) in pigs as a key indicator of nutritional status, metabolic health, reproductive performance, and overall well-being in commercial swine …