Multi-class wheat moisture detection with 5GHz Wi-Fi: A deep LSTM approach

Abstract

Moisture content of cereal grains is a highly important factor in safe storage and food processing. The existing detection methods are either time-consuming, sensitive to the environment, or have a high cost. In this paper, we propose DeepWMD, a deep LSTM network based system for multi-class wheat moisture detection. We first collect CSI amplitude and phase difference data to detect wheat moisture content. Then, we design the DeepWMD system with commodity Wi-Fi devices in the 5GHz band, including data preprocessing of collected CSI data, offline training, and online testing. Our experimental results verify the efficacy of the proposed DeepWMD system, and demonstrates that DeepWDM can achieve high-precision multi-class wheat moisture detection in different indoor storage environments.