Accurate real-time monitoring of cattle behavior is essential for enabling data-driven
decision-making in precision livestock farming. However, existing monitoring solutions
often rely on cloud-based processing or high-power hardware, which are impractical
for deployment in remote or low-infrastructure agricultural environments. There is a
critical need for low-cost, energy-efficient, and autonomous sensing systems capable of
operating independently at the edge. This paper presents a compact, sensor-integrated
system for real-time cattle behavior monitoring using an embedded vision sensor and
a TinyML-based inference pipeline. The system is designed for low-power deployment
in field conditions and integrates the OV2640 image sensor with the Sipeed Maixduino
platform, which features the Kendryte K210 RISC-V processor and an on-chip neural
network accelerator (KPU). The platform supports fully on-device classification of cattle
postures using a quantized convolutional neural network trained on the publicly available
cattle behavior dataset, covering standing and lying behavioral states. Sensor data is
captured via the onboard camera and preprocessed in real time to meet model input
specifications. The trained model is quantized and converted into a K210-compatible
.kmodel using the NNCase toolchain, and deployed using MaixPy firmware. System
performance was evaluated based on inference latency, classification accuracy, memory
usage, and energy efficiency. Results demonstrate that the proposed TinyML-enabled
system can accurately classify cattle behaviors in real time while operating within the
constraints of a low-power, embedded platform, making it a viable solution for smart
livestock monitoring in remote or under-resourced environments.