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An AI-Enabled Framework for Cacopsylla chinensis Monitoring and Population Dynamics Prediction

Ruijun Jing, Deyan Peng, Jingtong Xu, Zhengjie Zhao, Xinyi Yang, Yihai Yu, Liu Yang, Ruiyan Ma and Zhiguo Zhao ()
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Ruijun Jing: School of Software, Shanxi Agricultural University, Taiyuan 030800, China
Deyan Peng: School of Software, Shanxi Agricultural University, Taiyuan 030800, China
Jingtong Xu: College of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, China
Zhengjie Zhao: College of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, China
Xinyi Yang: College of Resources and Environment, Shanxi Agricultural University, Taiyuan 030800, China
Yihai Yu: School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
Liu Yang: College of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, China
Ruiyan Ma: College of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, China
Zhiguo Zhao: College of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, China

Agriculture, 2025, vol. 15, issue 11, 1-19

Abstract: The issue of pesticide and chemical residue in food has drawn increasing public attention, making effective control of plant pests and diseases a critical research focus in agriculture. Monitoring of pest populations is a key factor constraining the precision of pest management strategies. Low-cost and high-efficiency monitoring devices are highly desirable. To address these challenges, we focus on Cacopsylla chinensis and design a portable, AI-based detection device, along with an integrated online monitoring and forecasting system. First, to enhance the model’s capability for detecting small targets, we developed a backbone network based on the RepVit block and its variants. Additionally, we introduced a Dynamic Position Encoder module to improve feature position encoding. To further enhance detection performance, we adopt a Context Guide Fusion Module, which enables context-driven information guidance and adaptive feature adjustment. Second, a framework facilitates the development of an online monitoring system centered on Cacopsylla chinensis detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the Cacopsylla chinensis population , enabling trend prediction. We conduct feasibility validation experiments by comparing detection results with a manual survey. The experimental results show that the detection model achieves an accuracy of 87.4% for both test samples and edge devices. Furthermore, the population dynamics model yields a mean absolute error of 1.94% for the test dataset. These performance indicators fully meet the requirements of practical agricultural applications.

Keywords: Dynamic Position Encoder; Context Guide Fusion Module; population relationship model (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: 2025
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