PigTalk: An AI-Based IoT Platform for Piglet Crushing Mitigation

Abstract

On pig farms, many piglets die because they are crushed when sows roll from side to side or lie down. On average, 1.2 piglets are crushed by sows every day. To resolve the piglet mortality issue, this article proposes PigTalk, an artificial intelligence (AI) based Internet of Things (IoT) platform for detecting and mitigating piglet crushing. Through real-time analysis of the voice data collected in a farrowing house, PigTalk detects if any piglet screaming occurs, and automatically activates sow-alert actuators for emergency handling of the crushing event. We propose an audio clip transform approach to pre-process the raw voice data, and utilizes min-max scaling in machine learning (ML) to detect piglet screams. In our first contribution, the above data preprocessing method together with subtle parameter setups of the machine learning model improve the piglet scream detection accuracy up to 99.4%, which is better than the previous solutions (up to 92.8%). In our second contribution, we show how to design two cyber IoT devices, i.e., DataBank for data pre-processing and ML_device for real-time AI to automatically trigger actuators such as floor vibration and water drop to force a sow to stand up. We conduct analytic analysis and simulation to investigate how the detection delay affects the critical time period to save crushed piglets. Our study indicates that PigTalk can save piglets within 0.05 s with 99.93% of the successful rate. Such results are validated in a commercial farrowing house. PigTalk is a new approach that automatically mitigates piglet crushing, which could not be achieved in the past.