An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction
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...
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MDPI AG
2025-06-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/11/1210 |
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| author | Ruijun Jing Deyan Peng Jingtong Xu Zhengjie Zhao Xinyi Yang Yihai Yu Liu Yang Ruiyan Ma Zhiguo Zhao |
| author_facet | Ruijun Jing Deyan Peng Jingtong Xu Zhengjie Zhao Xinyi Yang Yihai Yu Liu Yang Ruiyan Ma Zhiguo Zhao |
| author_sort | Ruijun Jing |
| collection | DOAJ |
| description | 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 <i>Cacopsylla chinensis</i> 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 <i>Cacopsylla chinensis</i> detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the <i>Cacopsylla chinensis population</i>, 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. |
| format | Article |
| id | doaj-art-be218dad58f849a79b4c19ef57ce9fe2 |
| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-be218dad58f849a79b4c19ef57ce9fe22025-08-20T02:33:07ZengMDPI AGAgriculture2077-04722025-06-011511121010.3390/agriculture15111210An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics PredictionRuijun Jing0Deyan Peng1Jingtong Xu2Zhengjie Zhao3Xinyi Yang4Yihai Yu5Liu Yang6Ruiyan Ma7Zhiguo Zhao8School of Software, Shanxi Agricultural University, Taiyuan 030800, ChinaSchool of Software, Shanxi Agricultural University, Taiyuan 030800, ChinaCollege of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, ChinaCollege of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, ChinaCollege of Resources and Environment, Shanxi Agricultural University, Taiyuan 030800, ChinaSchool of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaCollege of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, ChinaCollege of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, ChinaCollege of Plant Protection, Shanxi Agricultural University, Taiyuan 030800, ChinaThe 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 <i>Cacopsylla chinensis</i> 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 <i>Cacopsylla chinensis</i> detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the <i>Cacopsylla chinensis population</i>, 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.https://www.mdpi.com/2077-0472/15/11/1210Dynamic Position EncoderContext Guide Fusion Modulepopulation relationship model |
| spellingShingle | Ruijun Jing Deyan Peng Jingtong Xu Zhengjie Zhao Xinyi Yang Yihai Yu Liu Yang Ruiyan Ma Zhiguo Zhao An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction Agriculture Dynamic Position Encoder Context Guide Fusion Module population relationship model |
| title | An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction |
| title_full | An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction |
| title_fullStr | An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction |
| title_full_unstemmed | An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction |
| title_short | An AI-Enabled Framework for <i>Cacopsylla chinensis</i> Monitoring and Population Dynamics Prediction |
| title_sort | ai enabled framework for i cacopsylla chinensis i monitoring and population dynamics prediction |
| topic | Dynamic Position Encoder Context Guide Fusion Module population relationship model |
| url | https://www.mdpi.com/2077-0472/15/11/1210 |
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