@article{Cheng2026, 
author = {Yue Cheng and Yifei Tong and Honghua Huan},
title = {Prediction of the growth in grower-finisher pigs using biologically constrained machine learning under small-sample data conditions},
year = {2026},
journal = {International Journal of Agricultural and Biological Engineering},
volume = {19},
number = {2},
pages = {88-102},
keywords = {biologically constrained machine learning, pig growth prediction, small-sample data, precision livestock farming},
url = {https://www.sciopen.com/article/10.25165/j.ijabe.20261902.10243},
doi = {10.25165/j.ijabe.20261902.10243},
abstract = {Accurate prediction of pig growth is essential for feed planning and market decisions in precision pig farming, but farm data are often small and fragmented. To address this challenge, a biologically constrained machine learning framework is proposed to predict the time required for grower-finisher pigs to reach the target weight of 100 kg under small sample conditions. By applying biologically constrained modifications to machine learning models (SC-GAM and Monotone XGBoost), the constrained models outperformed unconstrained baselines, which achieved higher accuracy (RMSE ≤ 4.73 d, ACC±7d ≥ 92%), greater robustness, and improved biological realism. An evaluation system was developed that combines traditional accuracy indicators with biologically grounded metrics. Practical applicability was examined via an on-farm shadow test on an independent batch. The models delivered reliable predictions that support finishing scheduling and feed-related management decisions. These findings highlight the potential of biologically constrained models to improve operational efficiency and reduce resource wastage in commercial pig farming.}
}