# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 6 Conclusions  
In the context of modern agricultural demands generated by large-scale planting, the spread and control of plant pests and diseases have become important research topics in agricultural production and plant protection, which are critical for improving production efficiency and ensuring crop health. However, existing studies often lack an in-depth analysis of the dynamic evolution of pest and disease spread and complex system modeling, especially the spatial and temporal dynamics of pest and disease spread, which have not been systematically quantitatively analyzed. This paper first identifies and classifies plant pest and disease early warning mechanisms. Based on environmental factors and symptomatology of affected plant parts, a dynamic model of pest and disease spread is established, and a systematic description of the plant pest and disease early warning mechanism system is provided. Secondly, a generalized GSPN model is constructed to analyze the spread process and influencing factors of pests and diseases. By utilizing the isomorphic relationship between Petri nets and Markov chains, the reachability graph is generated, and a Markov chain model is constructed. The model's validity and feasibility are verified using PIPE. On this basis, three performance indicators--place busy probability, place idle probability, and transition utilization rate--are calculated to identify the key factors and critical nodes in the pest and disease spread process, namely, information filtering (t\({}_{1}\)), mild infection (t\({}_{9}\)), moderate infection (t\({}_{10}\)), severe infection (t\({}_{11}\)), reaching treatment standards (t\({}_{12}\)), and treatment effectiveness assessment (t\({}_{21}\)). By changing the values of these key factors, the impact of these factors on the overall pest and disease evolution process is analyzed. The simulation results show that different infection rate combinations significantly affect the probability of meeting treatment criteria. Among them, the combination of a moderate infection rate and severe infection rate has the most significant impact on the probability of meeting treatment criteria, while the effect of the mild infection rate is relatively smaller. By considering the interactions between mild, moderate, and severe infection  
Figure 11: Figure grape downy mildew disease progression trend. rates, critical surfaces for different pest and disease early warning thresholds (0.005, 0.01, 0.015) are derived. The plant pest and disease early warning critical surfaces illustrate the probability distribution of reaching the respective early warning thresholds under different infection rate combinations, providing an intuitive basis for early warning decisions and treatment measures. In practice, the plant pest and disease diagnosis model based on the GSPN developed in this paper is designed to provide effective decision support for agricultural production and plant protection. This model can be used to monitor and alert for pest and disease situations in plant cultivation by simulating the probability of meeting treatment criteria under different infection rate combinations, helping farmers and agricultural management departments take appropriate control measures in a timely manner. Based on the characteristics of the spread and evolution of grape downy mildew, this study established a simplified GSPN model focusing on the analysis of leaf lesions. Experimental data were collected at a grape cultivation base in Kunming, Yunnan Province, China. The experimental results demonstrate that the predictions made by the GSPN model are consistent with the actual data collected. During the early warning and control of grape downy mildew, the model accurately identified the disease transmission process and severity, successfully predicting the occurrence of the disease. This experimental validation further confirms the practical applicability and value of the model in real-world agricultural environments. However, there are still some limitations in this study.