Work

Feature-driven optimization for growth and mortality prevention in poultry farms

Year: 2025

Type: article

Abstract: The poultry sector faces ongoing challenges in reducing mortality and improving growth performance under variable environmental and operational conditions. To address this, we developed and implemented a feature-driven optimization model to predict two key indicators: mortality and average weight (AvgWeight), based on five input variables-day, temperature, humidity, feed consumption, and water consumption. An 88-day dataset from over 20,000 Taiwan native broilers was preprocessed through outlier removal, normalization, and interpolation. Five machine learning models-Random Forest, Gradient Boosting Machine, Support Vector Machine, Linear Regression, and Neural Network (NN)-were initially evaluated. The baseline NN demonstrated superior multi-output accuracy and was further refined into three variants. Among them, the Ensemble NN-comprising five parallel networks-achieved RMSEs of 0.45 (Mortality) and 0.02 (AvgWeight), with Coefficient of Variation of RMSE values of 3.42 % and 1.62 %, respectively. Feature importance and sensitivity analyses identified "Day" as the most influential predictor for Mortality (importance: 20.391; sensitivity: 42.513), followed by feed (12.785; 13.285) and water (11.426; 13.648) consumption, while environmental variables had less impact under stable housing. Integrated into a MATLAB-based application, this intelligent system enables "what-if" scenario-offering practical decision support for poultry managers. By bridging traditional livestock management with deep learning-based soft sensors, this study contributes a scalable and accurate tool for advancing precision agriculture in poultry production.

Source: Poultry Science

DOI: 10.1016/j.psj.2025.105869

Authors: Suhendra, Hao-Ting Lin, Vincentius Surya Kurnia Adi, Asmida Herawati

Institutions: National Chung Hsing University, University of Bengkulu, National Research and Innovation Agency

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,600