A multicore and edge TPU-accelerated multimodal TinyML system for livestock behavior recognition

Zhang, Q and Kanjo, E ORCID: https://orcid.org/0000-0002-1720-0661, 2025. A multicore and edge TPU-accelerated multimodal TinyML system for livestock behavior recognition. IEEE Internet of Things Journal, 13 (1), pp. 666-677. ISSN 2327-4662

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Abstract

The advancement of technology has revolutionized the agricultural industry, transitioning it from labor-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 multimodal network for three tasks: image classification, object detection, and behavior recognition. The system is deployed and evaluated on commercial microcontrollers for real-time inference using embedded applications, demonstrating up to 270× model size reduction, less than 80 ms response latency, and on-par performance comparable to existing methods. The incorporation of the wireless communication technique allows for seamless data transmission between devices, benefiting use cases in remote locations with poor Internet connectivity. This work delivers a robust, scalable Internet of Things (IoT)-edge livestock monitoring solution adaptable to diverse farming needs, offering flexibility for future extensions.

Item Type:	Journal article
Publication Title:	IEEE Internet of Things Journal
Creators:	Zhang, Q. and Kanjo, E.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	January 2025
Volume:	13
Number:	1
ISSN:	2327-4662
Identifiers:	Number Type 10.1109/jiot.2025.3624811 DOI 2519644 Other
Rights:	2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	29 Jan 2026 10:36
Last Modified:	29 Jan 2026 10:36
URI:	https://irep.ntu.ac.uk/id/eprint/55146

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