# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 4 Simulation Analysis  
### Performance Analysis of Pest and Disease Spread Evolution Process Based on the GSPN Model  
By solving the above linear system of equations, the steady-state probabilities of the pest and disease spread evolution events in different states can be obtained. From the steady-state probabilities, the key areas for improving the system's operational efficiency can be identified, which is of significant practical importance for enhancing the efficiency of emergency decision-making in pest and disease outbreaks. First, solve the place busy probability, place idle probability, and the transition utilization rate to analyze the model's performance and identify the main factors and key nodes in the pest and disease spread process [28]. Subsequently, the average implementation rates of these key nodes are adjusted, and MATLAB is used to generate a trend graph of the steady-state probability changes, thus providing robust data support and a theoretical foundation for pest and disease control. Based on the evolution process of plant pest and disease spread, this study divides the entire process into three stages: the data observation stage, the disease assessment stage, and the treatment execution stage. The relevant \(\lambda\) values are reasonably set according to the characteristics and practical conditions of each stage. The basis for these settings is illustrated in Table 5.  
In the data observation stage, following the study by [PERSON] et al. [29], the data collection and processing rates in similar agricultural pest and disease monitoring projects were optimized based on actual sampling and processing times. Drawing upon the practical experience and data processing methodologies outlined in the literature, we set \(\lambda_{1}=18\). In consultation with agricultural experts, the setting of \(\lambda\) values for observing symptoms in various plant parts was determined by considering the difficulty and time required for observation. Experts indicated that symptom observation for roots and stems is conducted at a moderate pace, with \(\lambda_{2}=10\) and \(\lambda_{3}=12\), respectively. In contrast, the observation of symptoms in leaves, flowers, fruits, young shoots, and buds necessitates a more meticulous inspection, leading to the following \(\lambda\) values: \(\lambda_{4}=8,\ \lambda_{5}=7,\ \lambda_{6}=6,\ \lambda_{7}=9\), and \(\lambda_{8}=8\). In the disease assessment stage, referring to the work of [PERSON] et al., which classifies the severity of leek white tip disease into early, moderate, and severe stages, we set the corresponding \(\lambda\) values for disease symptoms in accordance with the gradual acceleration characteristics of disease spread discussed in the literature [30]. In the early stage of the disease, the symptoms in affected plants are not immediately apparent; however, as the disease progresses, the plants gradually exhibit disease susceptibility characteristics, ultimately leading to rapid wilting and death within a short period. Based on this, we set the infection rates for mild, moderate, and severe stages as \(\lambda_{9}=18,\ \lambda_{10}=8\), and \(\lambda_{11}=3\), respectively. The determination of whether the treatment standard has been met requires a comprehensive evaluation, for which \(\lambda_{12}=8\). In the treatment execution stage, environmental control and nutrient management, as foundational measures, generally require a longer period to gradually exhibit their effects. Accordingly, we set \(\lambda_{13}=7\) and \(\lambda_{14}=6\). [PERSON] et al. noted that physical and chemical methods (such as the use of chemical pesticides and fungicides) are commonly  
Figure 4: PIPE model simulation validation diagram. employed treatment approaches, which can yield significant effects in a short period, leading to faster disease control [31]. Therefore, we set \(\lambda_{15}=5\) and \(\lambda_{16}=4\). [PERSON] et al.