# 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
highlighted that biological control and field sanitation practices, as long-term measures, rely on the natural regulation and resistance enhancement of plants, and their effects appear gradually and more slowly [32]. Based on this, we set \(\lambda_{17}=3\) and \(\lambda_{18}=2\). Through the corresponding treatment measures, plants gradually recover their health, with \(\lambda_{19}=3\). The plant death rate is relatively slow, with \(\lambda_{20}=14\). Finally, the determination of the treatment effect is made with \(\lambda_{21}=9\). To ensure timely updates and processing of all data, the data feedback rate is rapid, and thus, \(\lambda_{22}=20\). Using Formulas (15), (16), and (20) from Section 3.2, the steady-state probabilities are calculated and shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mark** & **Steady-State Probability** & **Mark** & **Steady-State Probability** \\ \hline P(M1) & 0.013443 & P(M12) & 0.030248 \\ P(M2) & 0.008066 & P(M13) & 0.011523 \\ P(M3) & 0.006722 & P(M14) & 0.013443 \\ P(M4) & 0.010083 & P(M15) & 0.016132 \\ P(M5) & 0.011523 & P(M16) & 0.020165 \\ P(M6) & 0.013443 & P(M17) & 0.026887 \\ P(M7) & 0.008962 & P(M18) & 0.04033 \\ P(M8) & 0.010083 & P(M19) & 0.161321 \\ P(M9) & 0.033168 & P(M20) & 0.034569 \\ P(M10) & 0.070578 & P(M21) & 0.188208 \\ P(M11) & 0.188208 & P(M22) & 0.084694 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Steady-state probabilities. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Stage** & **\(\lambda\) Value** & **Specific Value** & **Source** \\ \hline \multirow{8}{*}{Data Observation Stage} & \(\lambda 1\) & 18 & [PERSON] et al., Experimental Data \\ \cline{2-3}  & \(\lambda 2\) & 10 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 3\) & 12 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 4\) & 8 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 5\) & 7 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 6\) & 6 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 7\) & 9 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 8\) & 8 & Expert Recommendations \\ \hline \multirow{8}{*}{ Disease Assessment Stage} & \(\lambda 9\) & 18 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 10\) & 8 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 11\) & 3 & Appeltans et al. \\ \cline{2-3}  & \(\lambda 12\) & 8 & Expert Recommendations, Experimental Data \\ \hline \multirow{8}{*}{Treatment Execution Stage} & \(\lambda 13\) & 7 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 14\) & 6 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 15\) & 5 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 16\) & 4 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 17\) & 3 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 18\) & 2 & [PERSON] et al. \\ \cline{2-3}  & \(\lambda 19\) & 3 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 20\) & 14 & Expert Recommendations \\ \cline{2-3}  & \(\lambda 21\) & 9 & Expert Recommendations, Experimental Data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Basis for setting \(\lambda\) value.