# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
## 1 Introduction  
The modernization of agriculture is characterized by achieving economies of scale through modern cultivation practices during production and management processes. It serves as an effective strategy to enhance management efficiency, reduce production costs, and ultimately improve agricultural economic benefits [1]. This strategy demands a higher yield efficiency and rational resource utilization, while also introducing a series of modernization requirements, such as resource management, pest and disease control, and environmental protection. To meet these demands, modern agriculture increasingly relies on high-tech solutions, particularly artificial intelligence and automation technologies. Plant pests and diseases refer to various afflictions and infestations that impact the health and growth of plants, including damage caused by fungi, bacteria, viruses, insects, andother pests. These diseases and pests not only deteriorate the appearance and utility value of plants but can also, in severe cases, lead to plant death, resulting in significant economic losses in the agricultural industry [2]. Traditional methods for identifying plant diseases and pests mainly rely on manual visual diagnosis, using expert knowledge along with textual and visual information from books or online resources. With the proliferation of machine vision devices equipped with cameras and internet capabilities, computer vision technology has provided new avenues for modern crop pest and disease monitoring. This study primarily involves two types of machine learning algorithms: unsupervised learning methods, such as k-means clustering, and supervised learning methods using support vector machines. In the context of plant pest and disease diagnosis, k-means clustering is mainly used for data-driven early anomaly detection and lesion classification. [PERSON] et al. applied GLCM, KMC, and SVM algorithms to classify diseases in citrus leaf images, achieving a classification accuracy of 90% with their proposed strategy [3]. Similarly, [PERSON] and [PERSON] used KMC and SVM classifiers to categorize cucumber leaf diseases [4]. SVM, on the other hand, is commonly used for multi-class classification tasks involving known disease types and for classifying small-sample, high-dimensional data. For instance, [PERSON] et al. proposed a method based on drone aerial imagery and SVM classifiers. By analyzing near-infrared and visible light images, they effectively identified wheat plants affected by Sunn pest Eu-rygaster integriceps and mapped their distribution [5]. However, this technology requires significant manual intervention by experts or programmers, particularly during the feature extraction phase. It involves manually inputting extracted feature lists into traditional neural networks for data classification, which can be a labor-intensive process. The process is not only complex but also demands significant human and material resources. Despite the development of numerous disease diagnostic systems, their widespread application and adoption remain limited. Due to the complexity and operational challenges associated with machine learning classification techniques, deep learning algorithms have become a major focus of current research both domestically and internationally. [PERSON] and [PERSON] propose a Bayesian deep learning approach for plant disease detection, emphasizing the significance of uncertainty quantification in deep learning models to enhance reliability and generalization in real-world agricultural applications [6]. [PERSON] et al. utilized MobileNetV2 to classify various plant stress phenotypes in soybean, demonstrating its efficient ability to detect and classify biotic and abiotic stresses while maintaining computational efficiency. This approach supports real-time, on-field applications for plant disease diagnosis [7]. In addition, various network architectures such as Alex Net [8],VGG-16 [9], GoogLeNet [10], ResNet [11], and Inception [11] have been proposed and applied in disease recognition.