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 ()
Additional contact information
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
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/11/1210/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/11/1210/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:11:p:1210-:d:1669806

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().