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For the pulping process in a pulp & paper plant that uses wood as a raw material, it is important to have real-time knowledge about the moisture content of the woodchips so that the process can be optimized and/or controlled correspondingly to achieve satisfactory product quality while minimizing the consumption of energy and chemicals. Both destructive and non-destructive methods have been developed for estimating moisture content in woodchips, but these methods are often lab-based that cannot be implemented online, or too fragile to stand the harsh manufacturing environment. To address these limitations, we propose a non-destructive and economic approach based on 5 GHz Wi-Fi and use channel state information (CSI) to estimate the moisture content in woodchips. In addition, we propose to use statistics pattern analysis (SPA) to extract features from raw CSI data of amplitude and phase difference. The extracted features are then used for classification model building using linear discriminant analysis (LDA) and subspace discriminant (SD) classification. The woodchip moisture classification results are validated using the oven drying method.