Edge-AI Enabled Acoustic Monitoring and Spatial Localisation for Sow Oestrus Detection

TL;DR

This paper introduces an edge-AI system that uses sound to detect when sows are in heat, improving efficiency and animal welfare in pig farming.

Contribution

A low-cost, real-time edge-AI system for sow oestrus detection using acoustic monitoring and spatial localisation.

Findings

The system achieved 96.17% classification accuracy with 41 ms inference latency.

The system successfully mapped vocalisation events to individual gestation stalls using GCC-PHAT localisation.

The edge-intelligent system demonstrated robustness and low-cost deployment in laboratory pressure tests.

Abstract

Timely detection of sow oestrus is essential for enhancing reproductive efficiency and reducing non-productive days in large-scale pig farms. Traditional methods rely heavily on manual observation, which is labour-intensive and subjective. This study developed an intelligent edge monitoring system that uses non-contact acoustic sensing to capture sow vocalisations and artificial intelligence algorithms to automatically identify oestrus status and locate specific animals. The system was strictly validated against reproductive hormone levels to ensure scientific accuracy. By integrating “sound-location” technology, the system enables round-the-clock monitoring and individualised management without interfering with the sows’ natural behaviour. This approach enhances animal welfare while providing a low-cost, real-time, and efficient solution for precision management in modern smart livestock farming.

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone (P4). Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming.

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