Classifying vocal responses of broilers to environmental stressors via artificial neural network

Abstract

Detecting early-stage stress in broiler farms is crucial for optimising growth rates and animal well-being. This study aims to classify various stress calls in broilers exposed to cold, heat, or wind, using acoustic signal processing and a transformer artificial neural network (ANN). Two consecutive trials were conducted with varying amounts of collected data, and three ANN models with the same architecture but different parameters were examined. The impacts of adding broiler age data as an input attribute and varying input audio waveform lengths on model performance were assessed. Model performance improved with the inclusion of broiler age and longer audio waveforms when trained on smaller datasets. Additionally, the study evaluated the impact of majority vote decision-making across the three ANN model sizes, showing improvement in mean average precision (mAP), particularly for models with shorter audio inputs. Overall, the largest ANN model achieved the highest mAP score of 0.97 for the larger dataset, with small variations among different model sizes. These findings highlight the potential of using a single model to accurately classify multiple types of broiler stress calls. By enhancing the timing of human intervention during critical growth stages, the proposed method may significantly improve broiler welfare and farm management efficiency.