Poster: DNN Models for Underground Root Tuber Image Reconstruction using WiFi CSI

Non-invasive monitoring of underground biomass like root tubers is vital for smart agriculture. We present a wireless sensing system using Wi-Fi Channel State Information (CSI) from a low-cost ESP32 mesh network for high-resolution underground tuber imaging. Our approach uses synchronized CSI data collection and deep neural network (DNN) models including UNet, FCN and DeepLabV3+ for image reconstruction. We describe the testbed, data processing, experiments and DNN models in this paper. Comparative results show DNN models using the CSI data significantly outperforms those using traditional RSSI data. The DeepLabV3+ model using CSI data achieves the best imaging accuracy with an IoU of 0.6971, demonstrating the potential of WiFi CSI for fine-grained underground tuber sensing.