Comparative Analysis of MobileNetV2 Inference Performance on CPU, GPU and TPU for Agricultural Object Classification in Embedded Systems
Keywords:
Precision agriculture, MobileNetV2, TPU, GPU, CPU, Embedded systemsAbstract
The advancement of smart agriculture requires efficient computer vision solutions capable of operating on low-cost, low-power devices. This paper presents a comparative analysis of the inference performance of the MobileNetV2 model across different hardware architectures: a desktop CPU (Ryzen 5 5600G), a GPU (NVIDIA GTX 1070), and an embedded system equipped with a TPU (Google Coral Dev Board). The study evaluated inference time and frames per second (FPS) in both batch and online processing modes using datasets of agricultural insects, such as the whitefly.
The results demonstrate that the GPU is the fastest architecture for batch inference, reaching up to 683.28 FPS with a 97.93% accuracy rate. In contrast, Coral TPU proved to be the most efficient for online inference, achieving up to 348.96 FPS with 91.76% accuracy, whereas the CPU showed intermediate performance and lower energy efficiency. These findings confirm the viability of deploying MobileNetV2 for agricultural applications on embedded devices, highlighting that hardware selection depends strictly on the application: GPUs are ideal for massive data processing, while TPUs are optimal for real-time, edge-computing deployments in the field.
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