Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net

With the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warnin...

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Main Authors: Wenxue Ran, Qilian Tang
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/15/12/6656
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author Wenxue Ran
Qilian Tang
author_facet Wenxue Ran
Qilian Tang
author_sort Wenxue Ran
collection DOAJ
description With the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warning mechanisms. This study first conducted a detailed identification and classification of plant disease and pest warning mechanisms, and established a dynamic model of disease and pests based on the environmental factors and symptoms of affected areas. On this basis, using the isomorphism relationship between generalized stochastic Petri nets and Markov chains, a plant disease and pest diagnosis model based on generalized stochastic Petri nets and an equivalent Markov chain model were constructed. The simulation results show that different combinations of infection rates have a significant impact on the probability of meeting treatment standards, with the combination of moderate and severe infection rates having the greatest impact on the probability of meeting treatment standards, while the impact of mild infection rates is relatively small. By comprehensively analyzing the interaction between mild, moderate, and severe infection rates, the critical zone surface under different disease and pest warning thresholds was obtained. Through actual data verification, the generalized stochastic Petri net model can effectively quantify the dynamic characteristics of disease and pest propagation. Combined with the equivalent analysis of Markov chains, it can provide key thresholds and decision support for disease and pest warning. This method provides a theoretical basis for automated monitoring and precise control of pests and diseases in large-scale agricultural planting, and it has high practical application value.
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spelling doaj-art-0a16798247254a33b6db25eff4e7b6cc2025-08-20T03:26:15ZengMDPI AGApplied Sciences2076-34172025-06-011512665610.3390/app15126656Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri NetWenxue Ran0Qilian Tang1School of Logistics and Management Engineering, Yunnan University of Finance and Economics, Kunming 650221, ChinaSchool of Logistics and Management Engineering, Yunnan University of Finance and Economics, Kunming 650221, ChinaWith the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warning mechanisms. This study first conducted a detailed identification and classification of plant disease and pest warning mechanisms, and established a dynamic model of disease and pests based on the environmental factors and symptoms of affected areas. On this basis, using the isomorphism relationship between generalized stochastic Petri nets and Markov chains, a plant disease and pest diagnosis model based on generalized stochastic Petri nets and an equivalent Markov chain model were constructed. The simulation results show that different combinations of infection rates have a significant impact on the probability of meeting treatment standards, with the combination of moderate and severe infection rates having the greatest impact on the probability of meeting treatment standards, while the impact of mild infection rates is relatively small. By comprehensively analyzing the interaction between mild, moderate, and severe infection rates, the critical zone surface under different disease and pest warning thresholds was obtained. Through actual data verification, the generalized stochastic Petri net model can effectively quantify the dynamic characteristics of disease and pest propagation. Combined with the equivalent analysis of Markov chains, it can provide key thresholds and decision support for disease and pest warning. This method provides a theoretical basis for automated monitoring and precise control of pests and diseases in large-scale agricultural planting, and it has high practical application value.https://www.mdpi.com/2076-3417/15/12/6656diseases and pestsearly warning mechanismgeneralized stochastic Petri netMarkov chainbotany
spellingShingle Wenxue Ran
Qilian Tang
Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
Applied Sciences
diseases and pests
early warning mechanism
generalized stochastic Petri net
Markov chain
botany
title Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
title_full Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
title_fullStr Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
title_full_unstemmed Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
title_short Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
title_sort research on plant disease and pest diagnosis model based on generalized stochastic petri net
topic diseases and pests
early warning mechanism
generalized stochastic Petri net
Markov chain
botany
url https://www.mdpi.com/2076-3417/15/12/6656
work_keys_str_mv AT wenxueran researchonplantdiseaseandpestdiagnosismodelbasedongeneralizedstochasticpetrinet
AT qiliantang researchonplantdiseaseandpestdiagnosismodelbasedongeneralizedstochasticpetrinet