# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 1 Introduction
Although significant progress has been made in crop disease identification using deep learning methods, there are still limitations, including limited generalization ability, low accuracy in early and small lesion detection, and large computational requirements that make practical deployment difficult [12]. Furthermore, the significant symptom variability at different disease stages, symptom similarities among different diseases, and the coexistence of multiple diseases add complexity to the diagnostic process. In the agricultural domain, the application of Petri nets has been gaining increasing attention, especially in agricultural planting and management systems. [PERSON] et al. developed a Petri net model based on an Internet of Things system to achieve real-time monitoring of eggplant cultivation, validating the system's effectiveness in precision agriculture [13]. [PERSON] et al. utilized the stochastic Petri net to construct a predictive model for soil fertility trends, demonstrating its effectiveness using existing soil fertility data and highlighting the potential of Petri nets in analyzing complex agricultural systems and providing decision support [14]. [PERSON] et al. applied a colored Petri net-based adaptive control approach in their study of an intelligent broad bean harvesting system, successfully achieving real-time optimization and decision support in the agricultural harvesting process [15]. These studies collectively demonstrate the potential of Petri nets in agricultural production, particularly in addressing the dynamics and complexities of agricultural production systems. In light of these challenges, this paper proposes a plant pest and disease diagnostic model based on generalized stochastic Petri nets (GSPNs). The GSPN demonstrates significant advantages in handling system dynamics and complexity, effectively modeling and analyzing the progression of pests and diseases and their interactions with environmental factors. This method enables the development of dynamic and highly adaptive diagnostic systems. Moreover, GSPN models exhibit greater flexibility and efficiency in addressing diagnostic challenges involving multiple pests and diseases in complex backgrounds. Most existing Petri net research focuses on areas such as power grid failures [16], emergency response [17], logistics scheduling [18], and the spread and evolution of infectious diseases [19], often conducting sensitivity analysis on a single indicator. However, there has been limited application of Petri nets in the field of plant pest and disease early warning mechanisms. Therefore, based on the construction of a dynamic pest and disease model, this research establishes a generalized Petri net model and an equivalent Markov chain model for the propagation and evolution of plant pests and diseases, utilizing the isomorphic relationship between GSPN and Markov chains. Finally, the equilibrium states and variation patterns of the pest and disease propagation system are analyzed using Markov chains and related mathematical methods. This research direction is expected to provide more effective technical support for plant pest and disease management and is of great significance in promoting the modernization and scaling up of the agricultural industry.