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Water is an important component of tree cells, so the study of moisture content diagnostic methods for live standing trees not only provides help for production management in agriculture, forestry and animal husbandry but also provides technical guidance for plant physiology. With the booming development of deep learning in recent years, the generative adversarial network (GAN) provides a method to solve the problem of insufficient manual sample collection and tedious and time-consuming labeling. In this paper, we design and implement a wireless acoustic sensor network (WASN)-based wood moisture content diagnosis system with the main objective of nondestructively detecting the water content of live tree trunks. Firstly, the WASN nodes sample the acoustic emission signals of tree trunk bark at high speed then calculate the characteristic parameters and transmit them wirelessly to the gateway; secondly, the Conditional Tabular Wasserstein GAN-Gradient Penalty-L (CTWGAN-GP-L) algorithm is used to expand the 900 sets of offline samples to 1800 sets of feature parameters to improve the recognition accuracy of the model, and the quality of the generated data is also evaluated using various evaluation metrics. Moreover, the optimal combination of features is selected from the expanded mixed data set by the random forest algorithm, and the moisture content recognition model is established by the LightGBM algorithm (GSCV-LGB) optimized by the grid search and cross-validation algorithm; finally, real-time long-term online monitoring and diagnosis can be performed. The system was tested on six tree species: Magnolia (Magnoliaceae), Zelkova (Ulmaceae), Triangle Maple (Aceraceae), Zhejiang Nan (Lauraceae), Ginkgo (Ginkgoaceae), and Yunnan Pine (Pinaceae). The results showed that the diagnostic accuracy was at least 97.4%, and the designed WASN model is fully capable of long-term deployment for observing tree transpiration.

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