MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition
This provides a cost-effective, scalable IoT-edge monitoring solution for livestock farming, particularly in remote areas with poor connectivity, though it is incremental in applying known techniques to this domain.
The paper tackles livestock behavior recognition by developing a multi-modal TinyML system that fuses accelerometer and vision data, achieving up to 270x model size reduction and under 80ms latency while maintaining performance comparable to existing methods.
The advancement of technology has revolutionised the agricultural industry, transitioning it from labour-intensive farming practices to automated, AI-powered management systems. In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity. This work presents a novel approach to animal activity recognition and movement tracking, leveraging tiny machine learning (TinyML) techniques, wireless communication framework, and microcontroller platforms to develop an efficient, cost-effective livestock sensing system. It collects and fuses accelerometer data and vision inputs to build a multi-modal network for three tasks: image classification, object detection, and behaviour recognition. The system is deployed and evaluated on commercial microcontrollers for real-time inference using embedded applications, demonstrating up to 270$\times$ model size reduction, less than 80ms response latency, and on-par performance comparable to existing methods. The incorporation of the TinyML technique allows for seamless data transmission between devices, benefiting use cases in remote locations with poor Internet connectivity. This work delivers a robust, scalable IoT-edge livestock monitoring solution adaptable to diverse farming needs, offering flexibility for future extensions.