High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion
IntroductionAphids are significant agricultural pests and vectors of plant viruses. Their Honeydew Excretion(HE) behavior holds critical importance for investigating feeding activities and evaluating plant resistance levels. Addressing the challenges of suboptimal efficiency, inadequate real-time ca...
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| Format: | Article |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1609222/full |
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| author | Zhongqiang Song Jiahao Shen Qiaoyi Liu Wanyue Zhang Ziqian Ren Kaiwen Yang Xinle Li Jialei Liu Fengming Yan Wenqiang Li Yuqing Xing Lili Wu |
| author_facet | Zhongqiang Song Jiahao Shen Qiaoyi Liu Wanyue Zhang Ziqian Ren Kaiwen Yang Xinle Li Jialei Liu Fengming Yan Wenqiang Li Yuqing Xing Lili Wu |
| author_sort | Zhongqiang Song |
| collection | DOAJ |
| description | IntroductionAphids are significant agricultural pests and vectors of plant viruses. Their Honeydew Excretion(HE) behavior holds critical importance for investigating feeding activities and evaluating plant resistance levels. Addressing the challenges of suboptimal efficiency, inadequate real-time capability, and cumbersome operational procedures inherent in conventional manual and chemical detection methodologies, this research introduces an end-to-end multi-target behavior detection framework. This framework integrates spatiotemporal motion features with deep learning architectures to enhance detection accuracy and operational efficacy.MethodsThis study established the first fine-grained dataset encompassing aphid Crawling Locomotion(CL), Leg Flicking(LF), and HE behaviors, offering standardized samples for algorithm training. A rapid adaptive motion feature fusion algorithm was developed to accurately extract high-granularity spatiotemporal motion features. Simultaneously, the RT-DETR detection model underwent deep optimization: a spline-based adaptive nonlinear activation function was introduced, and the Kolmogorov-Arnold network was integrated into the deep feature stage of the ResNet50 backbone network to form the RK50 module. These modifications enhanced the model’s capability to capture complex spatial relationships and subtle features.Results and discussionExperimental results demonstrated that the proposed framework achieved an average precision of 85.9%. Compared with the model excluding the RK50 module, the mAP50 improved by 2.9%, and its performance in detecting small-target honeydew significantly surpassed mainstream algorithms. This study presents an innovative solution for automated monitoring of aphids’ fine-grained behaviors and provides a reference for insect behavior recognition research. The datasets, codes, and model weights were made available on GitHub (https://github.com/kuieless/RAMF-Aphid-Honeydew-Excretion-Behavior-Recognition). |
| format | Article |
| id | doaj-art-f00a7ea86a9d43859cafea8eba92afdf |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-f00a7ea86a9d43859cafea8eba92afdf2025-08-20T02:42:46ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.16092221609222High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusionZhongqiang Song0Jiahao Shen1Qiaoyi Liu2Wanyue Zhang3Ziqian Ren4Kaiwen Yang5Xinle Li6Jialei Liu7Fengming Yan8Wenqiang Li9Yuqing Xing10Lili Wu11College of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Computing, City University of Hong Kong, Hong Kong SAR, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Plant Protection, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Plant Protection, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaCollege of Science, Henan Agricultural University, Zhengzhou, Henan, ChinaIntroductionAphids are significant agricultural pests and vectors of plant viruses. Their Honeydew Excretion(HE) behavior holds critical importance for investigating feeding activities and evaluating plant resistance levels. Addressing the challenges of suboptimal efficiency, inadequate real-time capability, and cumbersome operational procedures inherent in conventional manual and chemical detection methodologies, this research introduces an end-to-end multi-target behavior detection framework. This framework integrates spatiotemporal motion features with deep learning architectures to enhance detection accuracy and operational efficacy.MethodsThis study established the first fine-grained dataset encompassing aphid Crawling Locomotion(CL), Leg Flicking(LF), and HE behaviors, offering standardized samples for algorithm training. A rapid adaptive motion feature fusion algorithm was developed to accurately extract high-granularity spatiotemporal motion features. Simultaneously, the RT-DETR detection model underwent deep optimization: a spline-based adaptive nonlinear activation function was introduced, and the Kolmogorov-Arnold network was integrated into the deep feature stage of the ResNet50 backbone network to form the RK50 module. These modifications enhanced the model’s capability to capture complex spatial relationships and subtle features.Results and discussionExperimental results demonstrated that the proposed framework achieved an average precision of 85.9%. Compared with the model excluding the RK50 module, the mAP50 improved by 2.9%, and its performance in detecting small-target honeydew significantly surpassed mainstream algorithms. This study presents an innovative solution for automated monitoring of aphids’ fine-grained behaviors and provides a reference for insect behavior recognition research. The datasets, codes, and model weights were made available on GitHub (https://github.com/kuieless/RAMF-Aphid-Honeydew-Excretion-Behavior-Recognition).https://www.frontiersin.org/articles/10.3389/fpls.2025.1609222/fullhoneydew excretion detectionaphid behavior recognitionrapid adaptive motion feature fusionKolmogorov-Arnold networksRT-DETR-RK50 |
| spellingShingle | Zhongqiang Song Jiahao Shen Qiaoyi Liu Wanyue Zhang Ziqian Ren Kaiwen Yang Xinle Li Jialei Liu Fengming Yan Wenqiang Li Yuqing Xing Lili Wu High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion Frontiers in Plant Science honeydew excretion detection aphid behavior recognition rapid adaptive motion feature fusion Kolmogorov-Arnold networks RT-DETR-RK50 |
| title | High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion |
| title_full | High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion |
| title_fullStr | High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion |
| title_full_unstemmed | High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion |
| title_short | High-throughput end-to-end aphid honeydew excretion behavior recognition method based on rapid adaptive motion-feature fusion |
| title_sort | high throughput end to end aphid honeydew excretion behavior recognition method based on rapid adaptive motion feature fusion |
| topic | honeydew excretion detection aphid behavior recognition rapid adaptive motion feature fusion Kolmogorov-Arnold networks RT-DETR-RK50 |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1609222/full |
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