## 1 Introduction  
Forests are the largest ecosystem on Earth that human beings rely on for survival. They are also the most important barrier to protecting and ameliorating the ecological environment. In general, forests are made up of trees, shrubs, and herbs; the metabolism of forest plants depends on the orderly movement of water in their body [1]. In order to maintain normal growth, plants require a certain amount of water to support the conservation of energy in the ecosystem. However, for the interior water of plants, more is not always better [2; 3; 4]. Hence, the design and research of an effective and efficient real-time moisture content (MC) detection method for forest plants have drawn increasing attention recently, such as \(\gamma\)-ray [5], the resistance method [6], magnetic resonance imaging (MRI) [\(\gamma\)], time domain reflectometry (TDR) [8], frequency domain capacitance (FD) [9], etc. Among these existing methods, there are still some defects occurring unavoidably. The ray method has potential security risks; the resistance of the trunk is not intensely related to its moisture; MRI is an inconvenience in field application and is costly; TDR is a destructive testing method and is likely to cause considerable errors; and FD tends to be affected by the variation of trunk diameter [10]. Moreover, most of these achievements still follow manual or semi-automatic approaches, which poses an open challenge that should be tackled at full stretch to accomplish a fully autonomous forest monitoring system. Meanwhile, the Internet of Things (IoT) is a very promising technology in the forestry engineering field, especially for the environment and plant growth monitoring [11; 12; 13], as shown in Figure 1. Among these, the wireless acoustic sensor network (WASN) has gained some particular insights into the wildlife recognition field [14; 15]. It was deployed to monitor animal behavior in a non-intrusive manner by detecting and identifying different animal sounds. Additionally, the WASN has also been applied in wooden structural health monitoring, such as in our previous works [16]. It could capture the acoustic signal when wooden components suffer damage, and after data transmission, the sink would analyze and diagnose the details of the damage status. Here, we intend to study a non-destructive WASN-based forest monitoring system to accurately detect the tree trunk's MC level with convenience. This method mainly integrates the acoustic signal that is propagated along the tree bark, the soil humidity data, the ambient air temperature, and humidity data together to improve the measuring practicability and applicability. Additionally, an artificial intelligence technique is adopted to process the collected data and identify the MC level. In fact, recently, there are some references concerning the MC detection work on stones. [PERSON] et al. extracted six feature parameters from the original AE signals when compression tests were carried out on sandstone with different MC levels, and the identification of moisture was conducted using an SVM classifier [17]. [PERSON] et al. measured the effect of moisture on the AE characteristics of rocks and established a musical-staff-inspired MC detection model [18]. In [19], an AE multi-parameter analysis for dry and saturated sandstone with cracks under uniaxial compression was presented. Furthermore, soil physical characterization has employed AE tools as well; [PERSON] et al. developed a new soil multi-sensor fusion by combining mechanical, dielectric, and acoustic responses in layered soils for the estimation of soil water content, degree of compactness, and texture [20]. [PERSON] designed a vertical cone penetrometer with an acoustic sensor to correlate the acoustic signal with soil moisture [21]. As for timber detection, the AE technique certainly has many applications [22], whereas, for the MC measurement of woody plants, only a few studies have been introduced. [PERSON] et al. studied the use of AE for the detection of trees' vulnerability to drought [23]. [PERSON] et al.