## 2 Materials and Methods
### Data Acquisition and Preprocessing
#### 2.6.3 Feature Analysis  
Based on the above data collection steps, the dataset matrix of soil and AE feature parameters (900 \(\times\) 18) has been efficiently constructed, which should be used as the input of the mRMR selection module. Meanwhile, the corresponding MC level tags constituted the individual category vector accordingly. Here, if assuming that the number of optimal feature subset is 18, then the ranking order of all soil and AE features are lined up in Table 1 below. Furthermore, when the number of optimal features gradually increased from the minimum to the maximum, an SVM training and testing configured with default parameters, and the corresponding recognition accuracy would be obtained, as shown in Figure 9. The analysis indicates that when the top nine features are used to construct the feature subsets, the recognition accuracy reaches an optimum value of 92.3%, and after that, the accuracy declines rapidly. Therefore, the first nine features obtained by the mRMR selection module are adopted to participate in the MC diagnosis procedure afterward. \begin{table}
\begin{tabular}{c c} \hline
**Selection Algorithm** & **Ranking Order of the Features** \\ \hline mRMR & 1 Near/far-end energy difference; 2 near/far-end amplitude difference; 3 soil moisture content; 4 near-end amplitude; 5 near-end energy; 6 near-end ring count number; 7 near/far-end duration difference; 8 air humidity; 9 near-end duration; 10 near-end rise time; 11 far-end rise time; 12 near/far-end ring count number difference; 13 near/far-end rise time difference; 14 far-end ring count number; 15 far-end amplitude; 16 air temperature; 17 far-end duration; 18 far-end energy. \\ \hline \end{tabular}
\end{table} 
Table 1: Feature selection result of the mRMR. Figure 8: AE results of wood samples with different MC levels: (**a**) amplitude of the near AE point; (**b**) amplitude of the far AE point; (**c**) duration of the near AE point; (**d**) duration of the far AE point; (**e**) energy of the near AE signal; (**f**) energy of the far AE signal. 2.6.4. The FA-ISSA-SVM Diagnosis Algorithm  
The sparrow search algorithm (SSA) proposed by reference [36] is an efficient swarm intelligence optimization mechanism. Compared with other methods, the SSA has the advantage of high searching accuracy, good stability, and strong robustness, but it also has the shortcoming of slow convergence speed. Based on this, the SSA algorithm was applied to the parameter optimization of the SVM, and a firefly perturbation strategy [37] was added to update the sparrow's positions to further enhance the working efficiency. Taking the highest recognition accuracy of the SVM as the optimization object, the optimum regularization factor and kernel parameters were selected to establish the FA-ISSA-SVM diagnostic model. The SSA mainly simulates the process of sparrow's foraging, and it divides the population into three groups: discoverer, follower, and scouter. Discoverers have high fitness, a wide search range, and guide the sparrows to search and forage; followers forage with the discoverers to gain greater fitness; and the scouter will sound an alarm when it sees danger, and then the entire population makes an anti-predation behavior. Among the three groups, discoverers and followers could exchange roles with each other, whereas their proportion in the population remains constant, i.e., discoverers usually take up 10-20% of the entire colony. Suppose there are N sparrows in a D-dimensional search space, the position of the K sparrow is \(Xk\) = [\(xk1\),..., \(xkd\),..., \(xkD\)], where \(xkd\) represents the position of the \(K\)-th sparrow in the d dimension. The discoverer should update its position as follow:  
\[{x_{kd}}^{t+1}= \begin{cases}{x_{kd}}^{t}\cdot\exp(\frac{-k}{a\cdot iter_{\max }})&R_{2}<ST\\ {x_{kd}}^{t}+Q\cdot L&R_{2}\geq ST\end{cases}