Deploying neural network models onto low-power miniaturized computers such as Micro-Controller Unit (MCU) is becoming increasingly important for edge intelligence (EdgeAI) applications due to its potential to reduce the reliance on cloud connectivity and enhance real-time processing capabilities. However, this process often requires compressing the neural network models to fit the limited memory and computational resources of MCUs while maintaining acceptable performance. In this paper, we explore various techniques for compressing neural network models, including quantization, pruning, and model optimization frameworks like TensorFlow Lite and CMSIS-NN. Additionally, it discusses the challenges associated with deploying compressed models onto MCUs, such as limited memory, computational constraints, and the need for hardware acceleration techniques. By addressing these challenges and leveraging efficient deployment strategies, deploying neural network models onto MCUs can significantly improve edge computing performance, reduce power consumption, and enhance scalability for a wide range of applications. We also consider real-time implantation of sensor nodes to capture images.