Digital Twin System of Pest Management Driven by Data and Model Fusion

Min Dai, Yutian Shen, Xiaoyin Li, Jingjing Liu, Shanwen Zhang and Hong Miao ()
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Min Dai: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Yutian Shen: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Xiaoyin Li: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Jingjing Liu: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Shanwen Zhang: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China
Hong Miao: College of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China

Agriculture, 2024, vol. 14, issue 7, 1-19

Abstract: Protecting crops from pests is a major issue in the current agricultural production system. The agricultural digital twin system, as an emerging product of modern agricultural development, can effectively achieve intelligent control of pest management systems. In response to the current problems of heavy use of pesticides in pest management and over-reliance on managers’ personal experience with pepper plants, this paper proposes a digital twin system that monitors changes in aphid populations, enabling timely and effective pest control interventions. The digital twin system is developed for pest management driven by data and model fusion. First, a digital twin framework is presented to manage insect pests in the whole process of crop growth. Then, a digital twin model is established to predict the number of pests based on the random forest algorithm optimized by the genetic algorithm; a pest control intervention based on a twin data search strategy is designed and the decision optimization of pest management is conducted. Finally, a case study is carried out to verify the feasibility of the system for the growth state of pepper and pepper pests. The experimental results show that the virtual and real interactive feedback of the pepper aphid management system is achieved. It can obtain prediction accuracy of 88.01% with the training set and prediction accuracy of 85.73% with the test set. The application of the prediction model to the decision-making objective function can improve economic efficiency by more than 20%. In addition, the proposed approach is superior to the manual regulatory method in pest management. This system prioritizes detecting population trends over precise species identification, providing a practical tool for integrated pest management (IPM).

Keywords: pest management; digital twin; algorithm; decision-making (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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