Data-driven radio frequency (RF) tomography has shown great potential to detect underground targets due to the capability of RF signals to penetrate soil. However, it is still challenging to achieve robust detection performance in dynamic environments. In this work, we design a cross-modal sensing system with RF and visual sensors and propose to train an RF sensing deep neural network (DNN) model following the cross-modal learning approach. We also propose to apply continual learning to automatically update the DNN model in a dynamic environment. Specifically, we design an environmental change detector and a one-shot fine-tuning module and integrate them into the DNN model to reconstruct the cross-section images of underground tubers even with significant changes in RF signals. Experimental results show that our approach achieves an average equivalent diameter error of 2.29 cm, 23.2% improvement upon the state-of-the-art approach. Our code and dataset are both made publicly available.

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.

We demonstrate a non-invasive Wi-Fi sensing system that uses channel state information (CSI) data and deep neural network (DNN) models to reconstruct the cross-section images of potato tubers underground. We design a Wi-Fi mesh network that can leverage both the space and frequency diversities of the wireless network. We apply a multi-branch convolutional neural network (CNN) model to perform data-driven image reconstruction. We have performed extensive experiments to build a Wi-Fi potato sensing dataset, and our demo and experimental evaluations show that the Wi-Fi system outperforms the state-of-the-art root tuber wireless sensing system in terms of image quality and estimation accuracy.